System, method and computer program product for performing unstructured information management and automatic text analysis, including a search operator functioning as a weighted and (WAND)

ABSTRACT

Disclosed is a system architecture, components and a searching technique for an Unstructured Information Management System (UIMS). The UIMS may be provided as middleware for the effective management and interchange of unstructured information over a wide array of information sources. The architecture generally includes a search engine, data storage, analysis engines containing pipelined document annotators and various adapters. The searching technique makes use of a two-level searching technique. A search query includes a search operator containing of a plurality of search sub-expressions each having an associated weight value. The search engine returns a document or documents having a weight value sum that exceeds a threshold weight value sum. The search operator is implemented as a Boolean predicate that functions as a Weighted AND (WAND).

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.10/449,265, filed on May 30, 2003, and issued as U.S. Pat. No. 7,146,361on Dec. 5, 2006.

TECHNICAL FIELD

This invention relates generally to information management systems and,more specifically, relates to systems, methods and computer programs forimplementing an unstructured information management system that includesautomatic text analysis and information searching.

BACKGROUND

The amount of textual data in modem society is continuously growinglarger. The reasons for this are varied, but one important driving forceis the widespread deployment of personal computer systems and databases,and the continuously increasing volume of electronic mail. The result isthe widespread creation, diffusion and required storage of document datain various forms and manifestations.

While the overall trend is positive, as the diffusion of knowledgethrough society is generally deemed to be a beneficial goal, a problemis created in that the amount of document data can far exceed theabilities of an interested person or organization to read, assimilateand categorize the document data.

While textual data may at present represent the bulk of document data,and is primarily discussed in the context of this patent application,increasingly documents are created and distributed in multi-media form,such as in the form of a document that contains both text and images(either static or dynamic, such as video clips), or in the form of adocument that contains both text and audio.

In response to the increasing volume of text-based document data, it hasbecome apparent that some efficient means to manage this increasingcorpus of document data must be developed. This field of endeavor can bereferred to as unstructured information management, and may beconsidered to encompass both the tools and methods that are required tostore, access, retrieve, navigate and discover knowledge in (primarily)text-based information.

For example, as business methods continue to evolve there is a growingneed to process unstructured information in an efficient and thoroughmanner. Examples of such information include recorded natural languagedialog, multi-lingual dialog, texts translations, scientificpublications, and others.

Commonly assigned U.S. Pat. No. 6,553,385 B2, “Architecture of aFramework for Information Extraction from Natural Language Documents”,by David E. Johnson and Thomas Hampp-Bahnmueller, describes a frameworkfor information extraction from natural language documents that isapplication independent and that provides a high degree of reusability.The framework integrates different Natural Language/Machine Learningtechniques, such as parsing and classification. The architecture of theframework is integrated in an easily-used access layer. The frameworkperforms general information extraction, classification/categorizationof natural language documents, automated electronic data transmission(e.g., e-mail and facsimile) processing and routing, and parsing. Withinthe framework, requests for information extraction are passed toinformation extractors. The framework can accommodate bothpre-processing and post-processing of application data and control ofthe extractors. The framework can also suggest necessary actions thatapplications should take on the data. To achieve the goal of easyintegration and extension, the framework provides an integration(external) application program interface (API) and an extractor(internal) API.

The disclosure of U.S. Pat. No. 6,553,385 B2 is incorporated herein bereference in so far as it does not conflict with the teachings of thisinvention.

What is needed is an ability to efficiently and comprehensively processdocumentary data from a variety of sources and in a variety of formatsto extract desired information from the documentary data for purposesthat include, but are not limited to, searching, indexing, categorizingand data and textual mining.

SUMMARY OF THE PREFERRED EMBODIMENTS

The foregoing and other problems are overcome, and other advantages arerealized, in accordance with the presently preferred embodiments ofthese teachings.

Disclosed herein is a Unstructured Information Management (UIM) system.Important aspects of the UIM include the UIM architecture (UIMA),components thereof, and methods implemented by the UIMA. The UIMAprovides a mechanism for the effective and timely processing ofdocumentary information from a variety of sources. One particularadvantage of the UIMA is the ability to assimilate and processunstructured information.

An aspect of the UIMA is that it is modular, enabling it to be eitherlocalized on one computer or distributed over more than one computer,and further enabling sub-components thereof to be replicated and/oroptimized to adapt to an unstructured information management task athand.

The UIMA can be effectively integrated with other applications that areinformation intensive. A non-limiting example is provided wherein theUIMA is integrated with a life sciences application for drug discovery.

Aspects of the UIMA include, without limitation, a Semantic SearchEngine, a Document Store, a Text Analysis Engine (TAE), StructuredKnowledge Source Adapters, a Collection Processing Manager and aCollection Analysis Engine. In preferred embodiments, the UIMA operatesto receive both structured information and unstructured information toproduce relevant knowledge. Included in the TAE is a common analysissystem (CAS), an annotator and a controller.

Also disclosed as a part of the UIMA is an efficient query evaluationprocessor that uses a two-level retrieval process.

Disclosed is a data processing system for processing stored data thatincludes data storage for storing a collection of data units and,coupled to the data storage, a search engine responsive to a query forretrieving at least one data unit from said data storage. The querycomprises a search operator comprised of a plurality of searchsub-expressions each having an associated weight value, and the searchengine returns a data unit having a weight value sum that exceeds athreshold weight value sum. In a preferred embodiment the data unitscomprise documents.

More specifically, the query comprises a Boolean predicate thatfunctions as a Weighted AND (WAND). The WAND takes as arguments a listof Boolean variables X₁, X₂, . . . , X_(k), a list of associatedpositive weights, w₁, w₂,. . . , W_(k), and a threshold θ, where:

-   (WAND) (X₁, w₁, . . . X_(k),w_(k), θ)    is true if:

${{\sum\limits_{1 \leq i \leq k}{x_{i}w_{i}}} \geq \theta},$where x_(i) is the indicator variable for X_(i), where

$x_{i} = \left\{ \begin{matrix}{1,{{if}\mspace{14mu} X_{i}\mspace{14mu}{is}\mspace{14mu}{true}}} \\{0,{{otherwise}.}}\end{matrix} \right.$

The WAND can be used to implement one of an (AND) function or an (OR)function via:AND (X₁, X₂, . . . X_(k))≡WAND(X₁, 1, X₂, 1, . . . X_(k), 1, k),andOR (X₁, X₂, . . . X_(k))≡WAND(X₁, 1, X₂, 1, . . . X_(k), 1, 1).

Also disclosed is a method for processing document data, and a computerprogram product embodied on a computer-readable medium that containsprogram code for directing operation of a text intelligence system incooperation with at least one application. The computer program productincludes a computer program segment for storing a collection of dataunits and a computer program segment implementing a search engine thatis responsive to a query for retrieving at least stored one data unit.The query comprises a search operator comprised of a plurality of searchsub-expressions each having an associated weight value, and where saidsearch engine returns a data unit having a weight value sum that exceedsa threshold weight value sum.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other aspects of these teachings are made more evidentin the following Detailed Description of the Preferred Embodiments, whenread in conjunction with the attached Drawing Figures, wherein:

FIG. 1 is a block diagram that presents an overview of the architectureof the unstructured information management system disclosed herein;

FIG. 2 is a block diagram that presents aspects of a primitive analysisengine;

FIG. 3 is a block diagram that presents aspects of an aggregate analysisengine;

FIG. 4A is flowchart depicting an example of workflow in a CommonAnalysis System (CAS), and may further be viewed as an example of aplurality of serially-coupled annotators that form a part of a textanalysis engine;

FIG. 4B shows an example of an alternate embodiment of coupledannotators, where there is at least two parallel annotator paths;

FIG. 5 is a table of exemplary type definitions;

FIG. 6 is a table of exemplary feature definitions;

FIG. 7 is a table showing an exemplary component list;

FIG. 8 is a flow chart depicting workflow generation;

FIG. 9 is a flow chart depicting workflow verification;

FIG. 10A depicts an example of relationships in a single inheritancetree;

FIG. 10B illustrates a data modeling example using multiple inheritance;

FIG. 11 is a block diagram that provides an overview of aspects of theCommon Analysis System;

FIG. 12 is a block diagram depicting additional relationships of a textanalysis engine;

FIG. 13 is a graphic depiction of an exemplary annotation structure;

FIG. 14 is a block diagram that depicts operation of annotators;

FIG. 15 is a block diagram indicating relationships between tokens andspans, and is an example of an inverted file system;

FIG. 16 is a block diagram that provides alternative representations forspan occurrences;

FIG. 17 is a diagram exemplifying a relationship with spans in apre-processing stage;

FIG. 18 is a flow chart describing pre-processing for discoveringrelations in text;

FIG. 19 is a block diagram presenting aspects of relationships betweenthe annotation index, a relation index, spans and arguments;

FIG. 20 is a block diagram presenting an example of views of alternativerepresentations of a document, and corresponding tokenization thereof;

FIG. 20A illustrates the derivation of a plurality of views viadifferent tokenizations of a document;

FIG. 21 is a relational diagram depicting aspects of a search usingviews;

FIG. 22 is a relational chart depicting aspects of a data model;

FIG. 23 is a block diagram depicting aspects of interfaces betweencomponents;

FIG. 24 is a block diagram providing aspects of pre-processing andrun-time;

FIG. 25 is a flow chart showing the relation of patterns and thethreshold weight;

FIG. 26 is an example of pseudo-code for an init( ) method of the WANDiterator;

FIG. 27 is an example of pseudo-code of a next( ) method of the WANDiterator;

FIG. 28 is a flowchart summarizing the flow of the WAND process;

FIG. 29 is a graph showing efficiency results for the WAND process;

FIG. 30 is a graph showing efficiency results for the WAND process;

FIG. 31 is a graph showing efficiency results for the WAND process;

FIG. 32 is a block diagram depicting an unstructured informationmanagement system in conjunction with a life sciences application;

FIGS. 33A and 33B illustrate exemplary pseudo-code for creating datathat is useful for explaining the operation of the Common AnalysisSystem (CAS), while FIG. 33C is an example of pseudo-code for CAS-baseddata access, and shows the use of iteration over tokens; and

FIG. 34 depicts an example of an n-gram (tri-gram) tokenization ofdocument text.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Disclosed herein is an Unstructured Information Management Architecture(UIMA). The following description is generally organized as follows:

-   I. Introduction-   II. Architecture Functional Overview    -   Document Level Analysis    -   Collection Level Analysis    -   Semantic Search Access    -   Structural Knowledge Access-   III. Architecture Component Overview    -   Search Engine    -   Document Store    -   Analysis Engine-   IV. System Interfaces-   V. Two-Level Searching-   VI. Exemplary Embodiment & Considerations    I. Introduction

The UIMA disclosed herein is preferably embodied as a combination ofhardware and software for developing applications that integrate searchand analytics over a combination of structured and unstructuredinformation. “Structured information” is defined herein as informationwhose intended meaning is unambiguous and explicitly represented in thestructure or format of the data. One suitable example is a databasetable. “Unstructured information” is defined herein as information whoseintended meaning is only implied by its form. One suitable example ofunstructured information is a natural language document.

The software program that employs UIMA components to implement end-usercapability is generally referred to in generic terms such as theapplication, the application program, or the software application. Oneexemplary application is a life sciences application that is discussedbelow in reference to FIG. 32.

The UIMA high-level architecture, one embodiment of which is illustratedin FIG. 1, defines the roles, interfaces and communications oflarge-grained components that cooperate to implement UIM applications.These include components capable of analyzing unstructured sourceartifacts, such as documents containing textual data and/or image data,integrating and accessing structured sources and storing, indexing andsearching for artifacts based on discovered semantic content.

FIG. 1 shows that the illustrated and non-limiting embodiment of theUIMA 100 includes a Semantic Search Engine 110, a Document Store 120, atleast one Text Analysis Engine (TAE) 130, at least one StructuredKnowledge Source Adapter 140, a Collection Processing Manager 150, atleast one Collection Analysis Engine 160, and Application logic 170. Inpreferred embodiments, the UIMA 100 operates to receive both structuredinformation 180 and unstructured information to produce relevantknowledge 195. The unstructured information may be considered to be acollection of documents 190, and can be in the form of text, graphics,static and dynamic images, audio and various combinations thereof. Agiven one of the documents that is ingested by the UIMA 100 is referredto as a document 190A.

Aspects of the UMA 100 shown in FIG. 1 are further shown in FIG. 2,where there is illustrated a Primitive Analysis Engine (PAE) 200 thatcan be a component part of the Text Analysis Engine 130. Included in thePAE 200 is a Common Analysis System (CAS) 210, an annotator 220 and acontroller 230. A second embodiment of a TAE 130 is shown in FIG. 3,wherein an Aggregate Analysis Engine (AAE) 300 is composed of two ormore component analysis engines 221, 222, 223 as well as the CAS 210,and implements the same external interface as the PAE 200. Furtherincluded in the aggregate analysis engine 300 is the controller 230, ananalysis sequencer 310 and an analysis structure broker 320. Thesefeatures will be discussed in greater depth below, and are thereforeonly presently introduced.

II. Architecture Functional Overview

It should be noted that the foregoing is but one embodiment, andintroductory. Therefore, aspects of the components of the UIMA 100disclosed in FIGS. 1, 2 and 3 may be varied. For example, the TAE 130may include appropriate engines for analysis of data other than text,such as voice or video.

While embodiments of the UIMA 100 extend to a variety of unstructuredartifacts, including without limitation: voice, audio and video; thediscussion herein is generally directed to UIMA 100 implementationsinvolving human language technologies in the form of text data. Further,as disclosed herein, elements of unstructured information for processingas documents 190A may include a whole text document, a text documentfragment, or even multiple documents. Therefore, the teachings hereinare only to be considered illustrative of aspects of the UIMA 100.

That is, the UIMA 100 may be realized in various embodiments havingvarious structures. For example, it may be considered advantageous toimplement the UIMA 100 as one large system, or as several smaller anddistributed systems. Such implementations may be varied depending onfactors such as the scale of the implementation as well as otherfactors.

An overview of aspects of the functions of the UIMA 100 are nowprovided. The aspects include both analysis and access functions.Analysis functions are divided into two classes, namely document-levelanalysis and collection-level analysis. Access functions are dividedinto semantic search access and structured knowledge access. Each ofthese function is introduced below.

II.A Document-Level Analysis

Document-level analysis is performed by the component processingelements referred to as the Text Analysis Engines (TAEs) 130. These areextensions of the generic analysis engine, and are specialized for text.Aspects of the TAE 130 may be considered analogous to the ProcessingResources disclosed in the GATE architecture by Cunningham et al., 2000(Uniform Language Resource Access and Distribution in the Context ofGATE-a General Architecture for Text Engineering ”, University ofSheffield, UK, 2000). In the UIMA 100, a TAE 130 is preferably arecursive structure that may be composed of sub-component or componentengines, each one performing a different stage of the application'sanalysis.

Examples of Text Analysis Engines 130 include language translators,document summarizers, document classifiers, and named entity detectors.Each TAE 130 is provided for discovering specific concepts (or “semanticentities”) otherwise unidentified or implicit in the document text 190A.

A TAE 130 inputs a document 190A and produces an analysis. The originaldocument 190A and the corresponding analysis are subsequentlyrepresented in a common structure referred to as the Common AnalysisSystem (CAS) 210. Generally, the CAS 210 is a data structure thatfacilitates the modeling, creation and retrieval of information for atleast one document 190A (see, for example, FIG. 11). The CAS 210 may belocalized or it may be distributed. Furthermore, the UIMA 100 supportsthe coordination of multiple CAS systems.

As used in the UIMA 100, and in general, annotations associate somemeta-data with a region in the original document 190A. Where thedocument 190A is a text document, for example, the annotation associatesmeta-data (e.g., a label) with a span of text in the document 190A bygiving directly or indirectly the span's start and end positions.Annotations in the CAS 210 are stand-off, meaning that the annotationsare maintained separately from the document itself. Stand-offannotations are generally considered to be more flexible than inlinedocument markup. However, in the UIMA 100 the annotations need not bethe only type of information stored in the CAS 210 for a given document190A. The CAS 210 may be used to represent any class of meta-dataelement associated with analysis of the document 190A, regardless ofwhether it is explicitly linked to some sub-component of the originaldocument 190A. The CAS 210 also allows for multiple definitions of thislinkage, as is useful for the analysis of images, video, or othernon-textual modalities. In general, there will be one CAS 210 associatedwith each document 190A.

An example of document level analysis is provided in FIG. 4A. In theexemplary workflow 400, an annotation pipeline includes a plurality ofcoupled annotators including a language identifier 410, a tokenizer 420,a sentence separation annotator 430, a part-of-speech (POS) tagger 440,a named entity recognition annotator 450, a parser 460, and a templatefilling annotator 470. Other non-limiting relationships that may be usedin addition to, or in substitution for, the exemplary annotators andsteps disclosed in FIG. 4A are provided in FIGS. 5-7. FIG. 8 and FIG. 9provide flowcharts representing aspects of Workflow Generation (FIG. 8),and Workflow Verification (FIG. 9). It should be noted that at leastsome of the various annotators 410-470 may appear in a different orderthan is illustrated in FIG. 4, e.g., in some circumstances the tokenizer420 may precede the language identifier 410.

However, it is not required that all of the annotators 410-470 bearranged in a serially coupled pipeline as shown in FIG. 4A. Forexample, FIG. 4B shows an example where a Dates annotator 415 isarranged in parallel with the Language ID and other annotators, andwhere the output of the Dates annotator 215 is taken directly back tothe CAS 210. This embodiment could be useful when ingesting a document190A written in a language, such as Kanji, that includes dates writtenusing Latin characters. Any number of parallel annotator paths, andnumbers of annotators per parallel path, can be provided (e.g., theDates annotator 415 may be followed by a serially coupled Timeannotator). Furthermore, the output of a given parallel annotator pathneed not be taken directly back to the CAS 210, but could be fed backinto another annotator path.

It should be noted that there may be more than one CAS 210 associatedwith a given document 190A, i.e., different TAEs 130 can use differentCASs 210. As an example, one TAE 130 may provide a translation of adocument 190A into a different language, using one CAS 210, whileanother TAE 130 may provide a summary of the same document 190, using adifferent CAS 210. Alternatively, a plurality of TAEs 130 can use thesame CAS 210 for the same document 190A.

The analysis represented in the CAS 210 may be considered to be acollection of meta-data that is enriched and/or refined (such as bydiscarding irrelevant data) as it passes through successive stages ofanalysis. At a specific stage of analysis, for example, the CAS 210 mayinclude a deep parse. A named-entity detector (450) receiving the CAS210 may consider the deep parse to identify named entities. The namedentities may be input to an analysis engine 130 that produces summariesor classifications based on a plurality of the documents 190A, e.g.,those documents 190A that refer to U.S. Presidents, or that refer tobusiness leaders in one or more business areas.

In the presently preferred embodiment the CAS 210 provides a generalobject-based document representation with a hierarchical type systemsupporting single inheritance. An example of an inheritance structure1000 is provided in FIG. 10A. In FIG. 10A the type system 1010 includesvarious sub-types, such as in the non-limiting examples provided,annotation 1020, parts of speech (POS) 1030, LangID 1040 and TravelPlan1050. These types (or sub-types) 1020, 1030, 1040 1050 may be furtherbroken down as is appropriate (e.g., variants of the sub-type LangID1040 include an English language sub-type 1040A, further including US,UK and Australia). In general, the type system 1010 provides a datamodel for the analysis of textual documents using the CAS 210.

However, the CAS 210 is not limited to the use of single inheritance,and FIG. 10B shows an example of data modeling using multipleinheritance. In this case the structure is not an inheritance tree, buta directed acyclic graph. Standard techniques, such as those in C++ orArtificial Intelligence, can be used to specify the operational anddeclarative semantics for multiple inheritance.

In either case (single or multiple inheritance) an example annotator maybe interested only in finding sentence boundaries and types, e.g. toinvoke another set of annotators for classifying pragmatic effects in aconversation.

Object-based representation with a hierarchical type system supportingsingle inheritance includes data creation, access and serializationmethods designed for the efficient representation, access and transportof analysis results among TAEs 130, and between TAEs 130 and other UIMAcomponents or applications. Elements in the CAS 210 may be indexed forfast access. The CAS 210 has been implemented in C++ and Java withserialization methods for binary, as well as with XML formats formanaging the tradeoff between efficiency and interoperability. Anexample of the relations of the CAS 210 with components of the UIMA 100is given in FIG. 11. In FIG. 11, in addition to the CAS 210, the TypeSystem 1110 and the Index Repository 1120 are shown, as is an Iterator1125. In general, the Type System 1110 specifies constraints onworkflow, not the annotator order per se, e.g., in FIG. 4A the Lang_IDannotator 410 should precede the parts of speech (POS) annotator 440.The Index Repository 1120 provides storage for pointers enabling certaininformation to be located in the document 190A, such as by specifyingthe locations of dates and proper names in the current document 190A.Further UIMA components 1130, 1140 and 1150 are shown, as well as anAnalysis Structure Broker (ASB) 320, discussed below.

II.B Collection-Level Analysis

Preferably, documents are gathered by the application 170 and organizedinto collections, such as the collection 190 shown in FIG. 1.Preferably, the UIMA 100 includes a Collection Reader interface thatforms a part of the CPM 150. Implementations of the Collection Readerprovide access to collection elements 190, collection meta-data andelement meta-data. UIMA 100 implementations include a Document,Collection and Meta-data Store 120 that cooperates with the CollectionReader interface and manages multiple collections and their elements.However, those applications 170 that desire to manage their owncollections may provide an implementation of a Collection Reader tothose UIMA 100 components that require access to the collection data.

Collections 190 can be analyzed to produce collection level analysisresults. These results represent aggregate inferences computed over allor some subset of the documents 190A in a collection 190. The componentof an application 170 that analyzes an entire collection 190 is theCollection Analysis Engine (CAE)160. The CAE(s) 160 typically applyelement-level, or more specifically document-level analysis, to elementsof a collection, such as individual documents 190A, and then considerthe element analyses in performing aggregate computations.

Examples of collection level analysis results include sub-collectionswhere elements contain certain features, glossaries of terms with theirvariants and frequencies, taxonomies, feature vectors for statisticalcategorizers, databases of extracted relations, and master indices oftokens and other detected entities.

In support of the Collection Analysis Engine(s) 160, the UIMA 10includes the Collection Processing Manager (CPM) component 150. The CPM150 is primarily tasked with managing the application of a designatedTAE 130 to each document 190A accessible through the Collection Readerin the store 120. The Collection Analysis Engine 160 may provide, asinput to the CPM 150, a TAE 130 and a Collection Reader (not shown). TheCPM 150 applies the TAE 130 and returns the analysis, represented by aCAS 210, for each element 190 in the collection. To control the process,the CPM 150 provides administrative functions that include failurereporting, pausing and restarting.

At the request of the application's Collection Analysis Engine 160, theCPM 150 may be optionally configured to perform functions typical of UIMapplication scenarios. Non-limiting examples of UIM applicationfunctions include: filtering—that ensures that only certain elements areprocessed based on meta-data constraints; persistence—that storeselement-level analysis; indexing—that indexes documents using adesignated search engine indexing interface based on meta-data extractedfrom the analysis; and parallelization—that manages the creation andexecution of multiple instances of a TAE 130 for processing multipledocuments simultaneously utilizing available computing resources.

II.C. Semantic Search Access

As used herein a “semantic search” implies the capability to locatedocuments based on semantic content discovered by document or collectionlevel analysis, that is represented as annotations. To support asemantic search, the UIMA 100 includes search engine indexing and queryinterfaces.

One aspect of the indexing interface is support of the indexing oftokens, as well as annotations and particularly cross-over annotations.Two or more annotations are considered to cross-over one another if theyare linked to intersecting regions of the document.

Another aspect of the query interface is support for queries that may bepredicated on nested structures of annotations and tokens, in additionto Boolean combinations of tokens and annotations.

II.D. Structured Knowledge Access

As analysis engines 130 perform their functions they may consult a widevariety of structured information sources 180. To increase reusabilityand facilitate integration, the UIMA 100 includes the Knowledge SourceAdapter (KSA) interface 140.

The KSA 140 provides a layer of uniform access to disparate knowledgesources 180. They manage the technical communication, representationlanguage and ontology mapping necessary to deliver knowledge encoded indatabases, dictionaries, knowledge bases and other structured sources180 in a uniform manner. In the preferred embodiment the primaryinterface to a KSA presents structured knowledge 180 as instantiatedpredicates using, as one non-limiting format example, the KnowledgeInterchange Format (KIF) encoded in XML.

One aspect of the KSA 140 architecture involves the KSA meta-data andrelated services that support KSA registration and search. Theseservices include the description and registration of named ontologies.Ontologies are generally described by the concepts and predicates theyinclude. The KSA 140 is preferably self-descriptive, and can include asmeta-data those predicate signatures associated with registeredontologies that the KSA 140 can instantiate, as well as an indication ofany knowledge sources consulted.

Preferably, application or analysis engine developers can consult humanbrowseable KSA directory services to search for and find KSAs 140 thatinstantiate predicates of a registered ontology. The service may delivera handle to a web service or an embeddable KSA component 140.

III. Architectural Component Overview

III.A. Search Engine 110

The Search Engine 110 is responsible for indexing and query processing.The search engine 110 is distinguished from a search application, thatwould use the search engine 110 and that would add, for example, pageranking and presentation functions to provide a basic searchapplication.

The UIMA 100 supports the development of applications that leverage theintegration of text analysis and search. In addition to execution ofbasic Boolean search capabilities, these applications may require thesearch engine to provide two advanced capabilities, referred to as“Spans” and “Views.”

Spans: Semantic entities such as events, locations, people, chemicals,parts, etc., may be represented in text by a sequence of tokens, whereeach token may be a string of one or more alphanumeric characters. Ingeneral, a token may be a number, a letter, a syllable, a word, or asequence of words. The TAE 130 produces annotations over spans oftokens. For example, an annotation of type “location” may be used toannotate the span of tokens “1313 Mocking Bird Lane”, while anannotation of type “person” may be used to annotate the span of tokens“Bob Smith”.

FIG. 13 provides an example of an annotation structure showing nestedspans of tokens with various annotation types. In FIG. 13, for example,each token is shown as being one word.

Annotations may have features (i.e. properties). For example,annotations of type “location” may have a feature “owner” whose value isthe owner of the property at that location. The values of features maybe complex types with their own features; for example the owner of alocation may be an object of type “person” with features “name=John Doe”and “age=50.”

The UIMA-compliant Search Engine 110 supports the indexing ofannotations over spans of tokens, or “spans.” There are at present twopreferred ways in which this could be accomplished, discussed below.Briefly, inline annotations can be inserted in a CAS 210 in some format(e.g. XML) understood by the indexer 110, or the indexer 110 is capableof understanding standoff annotations found in the CAS 210.

Translation to Inline Annotations: In this approach, the application 170accommodates the input requirements of the search engine 110. Forexample, search engines such as Juru can index XML documents, and thenprocess queries that reference the XML elements. Consider in thefollowing example, that the document could be indexed:<Event><Person>John</Person> went to <City>Paris</City>.</Event>

Then, if a query were entered for an Event containing the city Paris,this document would match that query.

In order to use an XML-aware search engine 110 in the UIMA 100, theapplication 170 takes the standoff annotations produced by the TAE 130and encodes them inline as XML. The CAS 210 preferably defines a methodto generate this XML representation. The benefit of this approach isthat it can be made to work with any XML-aware search engine 110.

Search Engine Aware of Standoff Annotations: In this approach, thesearch engine's interface supports the concept of standoff (i.e.,non-inline) annotations over a document. Therefore, the output of theTAE 130 can be fed directly (or almost directly) into the search engine110, obviating the need for an intermediate representation such as XML.As an example, consider the document fragment and the locations of itstokens.

Washington D. C. is the Capital of the United States 1 2 3 4 5 6 7 8 910

It can be noted that the tokens have location definitions in theforegoing example (e.g., the tokens “Washington”, “D.”, “C.”) thatdiffer from those shown in FIG. 13. The preferred embodiment of the UIMA100 supports both types of token location definitions.

Assuming that the search engine 110 and TAE 130 agree on exactly thesame location space for this document, then the information may berepresented by the TAE 130 as follows:

$City 1 3 $Country 9 10

However, if the TAE 130 and search engine 110 disagree on how whitespace is counted, how punctuation is addressed, or are simply out ofalignment, then the annotations $City and $Country may not be indexedproperly.

Therefore, an equivalent XML representation is provided, wherein:<$City>Washington D.C. </City> is the capital of the <$Country>UnitedStates</$Country>.

XML parsing is generally more computationally expensive then theforegoing alternative. Preferably, this is mitigated by using anon-validating parser that takes into consideration that this may not bethe most limiting step of the pre-processing functions.

Further in consideration of XML, in some embodiments a disadvantage ofthe XML representation is that a TAE 130 may produce overlappingannotations. In other words, annotations are not properly nested.However, XML would not naturally represent overlapping annotations, andfurther mechanisms may be employed to provide a solution.

Also, consider the string of characters “airbag.” This is a compoundnoun for which an application may wish to index annotations from a TAE130 that distinguishes “air” from “bag.” If the search engine 110supports only one tokenization of a document, where “airbag” wasinterpreted as a single token, but a TAE 130 used a differenttokenization that treated “air” and “bag” distinctly, the application170 could not index annotations on “air” separately from annotations on“bag”, since the search engine's 110 smallest indexing unit in this casewas “airbag.”

For the example document fragment above, the annotations sent to theSearch Engine 110 would be:

$Token 0 9 $Token 11 12 $Token 13 14 $Token 16 17 $Token 19 21 $Token 2329 $Token 31 32 $Token 34 36 $Token 38 43 $Token 45 50 $City 0 14$Country 38 50

The “city” and “country” annotations have been specified using characteroffsets (that is their internal representation in the CAS 210). If thesearch engine 110 ultimately would prefer them to be specified usingtoken numbers, either the application or the search engine 110 couldperform the translation.

It should be noted that, in general, tokens can be single characters, orthey can be assemblages of characters.

Some of the benefits of this approach include the fact that there is noneed for expensive translations from a standoff annotation model to aninline annotation model, and back again. Also, overlapping annotationsdo not present a problem.

One embodiment of the relationship between the Search Engine 110, theTAE 130, and a series of annotators 1220, 1221, 1222 is provided in FIG.12. Also shown is the ASB 320, a User Interface (UI) 170A for theApplication 170, and a Text Analysis (TA) Resource Repository 130A thatreceives an output from the TAS 130.

FIG. 14 provides a representation of the operation of exemplaryannotators 1220, 1221, 1222 of FIG. 12 that operate at the document andat the word level. In this example the document-level languageidentifier 410 is followed by a detagger 415 (for identifying HTML tags,followed by the tokenizer 420, followed by the POS annotator 440,followed by the location identification annotator 445.

Relations

FIG. 15 shows a representation for inverted files for tokens 1510, 1520,1530 and spans 1550, 1560, 1570, while FIG. 16 is a diagram thatprovides alternative representations for span occurrences. In FIG. 16,an occurrence 1610 is defined as having a start location and endlocation 1620, or a start location and a length 1630. A Span 1650 isdefined as having at least a start token 1660 and an end token 1670,that are then further specified as to location.

FIGS. 17, 18 and 19 present examples of representing relations withspans in a pre-processing step executed by the TAE 130 to discoverrelations in the document. In the example provided in FIG. 17, spanscontaining relation arguments with the relation name “Inhibits” areannotated. In this example a first chemical compound has been identifiedas an Inhibitor, and a second chemical compound has been identified asbeing Inhibited, and the relationship is one of Inhibits. The annotationof the spans corresponds to terms with the argument roles “Inhibitor”and “Inhibited”, and the annotations over the spans are indexed.

A flow chart describing this process is provided in FIG. 18. In FIG. 18,a first step 1810 involves discovering relation text, i.e., discoveringa range of text in a document where a relation is expressed. A secondstep 1820 discovers argument text, i.e., discovering a range of text inthe document where each argument is expressed. For each relation andargument spans are determined at step 1830, the argument spans areordered in step 1840, and annotations are created for the relationshipspan and for each of its argument spans in step 1850. Labels areassigned and added to an index at step 1855, and relations are createdat step 1860 by linking argument annotations to relation annotations ina specified order.

FIG. 19 provides a graphic presentation of relationships with a spanindex. In FIG. 19, an annotation index 1910 incorporates a relationindex 1920 that relates to relation arguments 1930 that includesdocument identification 1940, where each document 190A includes spans1950 delineated by Start and End locations.

Locations and Search

In general, a set of token locations is monotonic. However, based on theforegoing discussion a set of token locations can be one of contiguousor non-contiguous, and a token or a set of tokens may be spanned by atleast two annotations.

An annotation type can be of any semantic type, or a meta-value. Thus,the search engine 110 may be responsive to a query that comprises atleast one of an annotation, a token, and a token in relation to anannotation.

The relationship data structure can contain at least one relationshipcomprised of arguments ordered in argument order, where a relationshipis represented by a respective annotation, and where the search engine110 can be further responsive to a query that comprises a specificrelationship for searching data store 120 to return at least onedocument having the specific relationship. The search engine 110 canfurther return at least one argument in a specific relationship. Thesearch engine 110 can further return a plurality of ordered arguments.At least one argument can comprise an argument annotation linked to theannotation. The search engine 110 can also return at least one argumentin response to a query that is not explicitly specified by the query. Anannotation can comprise a relation identifier, and the relationidentifier can be comprised of at least one argument. An argument thatcomprises the relation identifier can comprise, as examples, at leastone other annotation, a token, a string, a record, a meta-value, acategory, a relation, a relation among at least two tokens, and arelation among at least two annotations. The relation identifier canalso comprise a logical predicate.

In similar spirit, the relationship data structure (comprising arelationship name and arguments ordered in argument order), representedby a respective annotation, can appear in the search engine 110 queries.Such a query specifies a relationship structure (or a part of same) forsearching data store 120 to return at least one document having thespecified relationship. The search engine 110 can further return one ormore arguments in the specified relationship. When the search engine 110returns one or more of ordered arguments, each argument can comprise anargument annotation linked to the annotation. Note that in response to aquery the search engine 110 can also return at least one argument thatis not explicitly specified by the query.

An annotation of a relationship can include a relation identifier, e.g.,a logical predicate. Such annotation might also incorporate one or morearguments. An argument can comprise, as examples, at least one otherannotation, a token, a string, a record, a meta-value, a category, arelation, a relation among at least two tokens, and a relation among atleast two annotations.

Views

Acknowledging that different TAEs 130 may produce differenttokenizations of the same document(s), a UIMA-compliant Search Engine110 preferably supports different tokenizations, or different sets ofindexing units for the same documents. These different tokenizations mayresult in different “views” of a document. An example of views based on,or derived from, different tokenizations of a document 190A is providedin FIG. 20, wherein a first alternative representation 2010 and a secondalternative representation 2020 can result in a plurality of views,shown as views 2050, 2060, 2070, 2080.

In general, a view is an association of a document 190A with atokenization. Thus, a view can be represented by pairing the document190A identifier with the result of a tokenization. It can thus be seenthat a different view represents a different tokenization of a document190A. Referring to FIG. 20A, if TAE3 extends the tokenization of the setof tokens 2, e.g., by breaking words into stems and suffixes, thisresults in a new view (View 3).

FIG. 21 provides an illustration of aspects of searching with viewsusing Boolean operators 2100 with search expressions 2110, 2120, 2130for the different document views arising from different tokenizations ofa single source document.

The operation of a TAE 130 is preferably not predicated on pre-existingviews or decisions made by the application 170 regarding the relevanceof the content produced by the TAE 130. The UIMA 100 ensures that TAEs130 may be developed independently of the application 170 in which theyare deployed. Therefore, it is preferably the responsibility of theapplication 170 to create views. Preferably, if two TAEs 130 are run onthe same document 190A and produce results based on differenttokenizations, these results are not merged into a single view of thedocument. Accordingly, the application 170 provides the results of eachTAE 130 to the search engine 110 as a separate view.

In a presently preferred embodiment the search engine 110 is configuredto assimilate views of at least one of two levels. The first level is a“Shallow Understanding” level, where the Search Engine 110 treatsmultiple views of a document 190A as completely separate entities thatare related only in that they ultimately point to the same documenttext. Ideally, such a search engine 110 would report the document 190Aonly once in its results list, even if multiple views of that documentmatched a query. The second level is a “Deeper Understanding” level,where the search engine 110 is aware of views so that queries can spanmultiple views on the document 190A. For example, if in the query “X andY”, the term X appeared in view one of a document and the term Yappeared in view two of the same document, the document 190A would bereturned by the search engine 110. Note that the same query would notreturn the same document in the “Shallow Understanding” embodiment ofthe search engine 110.

A feature of the UIMA 100 is the ability to provide overlappingannotations, which provides a significant improvement over conventionalXML representations. An example of overlapping annotations, which canalso be referred to as “cross-over spans”, is the phrase “IBM datawarehousing products”, where a “double noun” annotation can be attachedto all consecutive word pairs: “IBM data”, “data warehousing” and“warehousing products”. Attaching labels of this type is very useful todifferentiate, for example, between a reading of “storing data createdby IBM” versus “IBM product for storing data”.

As has been discussed, preferably there is at least one inverted filesystem for storing tokens (see FIG. 15), and at least one inverted filesystem for storing, for each of the views, the annotations, a listcomprising occurrences of respective annotations and, for each listedoccurrence of a respective annotation, a set comprised of a plurality oftoken locations, where a given token location may be spanned by at leastone annotation (see FIG. 13).

As should be apparent, an inverted file system differs from aconventional file system at least in how individual files are indexedand accessed. In a conventional file system there may be simply alisting of each individual file, while in an inverted file system thereexists some content or meta-data, such as a token, associated in somemanner with a file or files that contain the content or meta-data. Forexample, in the conventional file system one may begin with a file nameas an index to retrieve a file, while in an inverted file system one maybegin with some content or meta-data, and then retrieve a file or filescontaining the content or meta-data (i.e., files are indexed by contentas opposed to file name).

The semantic search engine 110 may be responsive to a query thatcomprises a logical combination of at least two predicates, where afirst predicate pertains to a first view and a second predicate pertainsto a second view, and returns at least one document that satisfies thelogical combination of the predicates.

In the preferred embodiment of the invention the tokenizationcorresponds to, and is derived from, as examples, at least one of aplain text document, a language translation of a document, a summary ofa document, a plain text variant of a marked-up document, a plain textvariant of a HTML document and/or a multi-media document, such as onecontaining various multi-media objects such as text and an image, ortext and a graphical pattern, or text and audio, or text, image andaudio, or an image and audio, etc. The tokenization can be based onobjects having different data types. The tokenization may also bederived from an n-gram tokenization of a document. For example, FIG. 34depicts an example of a tri-gram tokenization of document text.

It should be noted that the UIMA 100 does not require multiple instancesof TAEs 130 to create multiple views of a document. Instead, one TAE 130may be used to create one view, and then reconfigured by selecting oneor more different annotators (see FIGS. 2, 3 and 4) and/or byre-arranging annotators, and then the document processed again to createanother view of the document.

III.B. Document Store

The Store 120, or Document Store 120, is the main storage mechanism fordocuments and document meta-data. Preferably, and not as a limitation,the Store 120 uses the Web Fountain (WF) model and assumes a simple APIthat allows document meta-data to be stored and accessed as key-valuepairs associated with documents.

Documents 190A in the Data Store 120 are preferably represented asinverted files with respect to a particular ordering of the documents inthe Data Store 120.

In the event that an application requires final or intermediate resultsof a Text Analysis Engine 130 (an analysis structure) to persist, theanalysis structure is preferably stored in the key-value structureassociated with the document 190A as meta-data in the Store 120. Theanalysis structure may be represented in a binary form as a BLOB thatcan be interpreted by the Common Analysis System (CAS) 210 component,although other forms may be used. In some embodiments, the storagemechanism for the search engine's index is the Document Store 120.

III.C Analysis Engine

This section provides an overview of aspects of the TAE 130, and thenconsiders further principles of operation for the TAE 130.

As was previously discussed, FIG. 2 presents a TAE 130 as an analysisengine 200, wherein a diagram of the framework of the analysis engine200 is provided. The UIMA 100 specifies an interface for an analysisengine 200; roughly speaking it is “CAS in” and “CAS out.” There areother operations used for filtering, administrative and self-descriptivefunctions, but the main interface takes a CAS 210 as input and providesa CAS 210 as output.

FIG. 3, also previously introduced, presents a TAE 130 as an aggregateanalysis engine 300, wherein a diagram of the framework of the aggregateanalysis engine 300 is provided. At run-time, an aggregate analysisengine 300 is given the order in which to execute the constituent textanalysis engines 221, 222, 223. The Analysis Structure Broker 320ensures that each text analysis engine 221, 222, 223 has access to theCAS 210 according to a specified sequence.

Preferably, any program that implements the interface shown in FIG. 2may be used as an analysis engine component in an implementation of UIMA100. However, as part of the UIMA 100, the analysis engine 200 mayinclude a framework that supports the creation, composition and flexibledeployment of primitive analysis engines 200 and aggregate analysisengines 300 on a variety of different system middleware platforms.Aspects of TAE 130 are now discussed in further detail.

The Text Analysis Engine (TAE) 130 is the component responsible fordiscovering and representing semantic content in text. The TAE 130 maybe tasked with the following exemplary activities: discovering syntacticand semantic entities represented by segments of text in a document (forexample, sentences, titles, paragraphs, people, places, events, times,biological entities, relations, chemical entities etc.); discoveringrelations in text; generating summaries of a document; translating adocument to a different language; and classifying a document intaxonomy.

Preferably, the TAE 130 takes as input a document 190A and produces ananalysis structure, that represents semantic information inferred fromthe text of document. The TAE 130 may also be initiated with a documentand an initial analysis structure that it modifies as a result ofoperation.

TAEs 130 are typically implemented by orchestrating a collection ofannotators 220 (which could also be interchangeably referred to as“miners”). Annotators 220 are components having distinctresponsibilities to use the original document 190A and/or prior analysisresults to discover and record new semantic content. Annotators 220 arepreferably, but are not required to be, organized in a pipelinearchitecture (see, for example, FIGS. 4A, 12 and 14), each of whichoperates on the document 190A, and on the results of prior annotators220 in the pipeline. This type of arrangement is introduced in FIG. 12.A further example of a series of annotators 220 used to identifylocations in a document appears in FIG. 14. As was previously noted,however, parallel arrangements of annotators 220 can also be provided,as is shown in FIG. 4B.

At a high level, consider that the TAE 130 is a component responsiblefor discovering semantic content in raw text. The TAE 130 may be used inan application's pre-processing phase to discover, for example, semanticentities in a corpus that represent locations, events, people and/orother similar types of information. At query time, the application 170may analyze the query to determine that the query is seeking informationrelated to some event that occurred at a certain time in a particularlocation. Preferably, the application 170 then queries the search engine110 to deliver documents that contain an event plus the given locationand time. To perform this query efficiently the application 170 expectsthat the semantic entities (particularly events in this case) discoveredin the preprocessing phase are indexed in the search engine 110.

It is preferred that the annotators 220 are developed without control orcommunication dependencies, otherwise they may be difficult tounderstand and reuse by more than one application 170.

The TAE 130 makes the insulation of annotator logic possible. Therefore,the TAE 130 may be considered as the container in which annotators 220are configured and deployed. Preferably, it is the role of the TAE 130to: orchestrate the flow of control and the communication betweenAnnotators 220; provide Annotators 220 with a uniform interface to TextAnalysis Resources (e.g. dictionaries); and, to publish a singleinterface for an application 170 to access the combined functionality ofa collection of annotators 220.

The TAE 130 specifies a functional interface. That is, the TAE 130accepts a document 190A (and optionally an initial analysis structure)as input and produces an analysis structure, that represents semanticcontent inferred from the document. The TAE 130 itself does not specifythe technical interface to this functionality. Access to the TAE 130 maybe provided through a variety of means.

While a TAE 130 may be directly included (co-located) within anapplication 170, the TAE 130 may also be deployed as a distributedservice (e.g. web services). A TAE Service wraps a TAE 130 and publishesa technical interface to the TAE 130. A deployed TAE Service listens forrequests to process documents, passes those documents on to the TAE 130,obtains the analysis structure produced by the TAE 130 and returns theanalysis structure to the caller.

Preferably, the UIMA 100 provides TAE Service implementations forseveral common distributed object technologies and protocols (e.g. SOAP,MQSeries, WebSphere, Mail). The UIMA 100 also preferably provides anaming service with which TAE Services are registered, so that clientscan locate needed services.

Generally, there are two types of TAEs 130: primitive 200 and aggregate300. A primitive TAE 200 is a container for one annotator 220. Itinsulates the annotator 220 from control and communication details andprovides the annotator 220 with a uniform interface to Text AnalysisResources. An aggregate TAE 300 delegates its work to one or more otherTAEs that may be either primitive 200 or aggregate TAEs 300. Theaggregate TAE 300 uses the Analysis Structure Broker (ASB) 320 to managecommunication between the constituent TAEs 221, 222, 223.

Common Analysis System 210

The Common Analysis System (CAS) 210 is provided as the common facilitythat all Annotators 220 use for accessing and modifying analysisstructures. Thus, the CAS 210 enables coordination between annotators220 and facilitates annotator 220 reuse within different applications170 and different types of architectures (e.g. loosely vs. tightlycoupled). Referring again to FIG. 14, the CAS 210 can be considered toconstrain operation of the various annotators 410-445, i.e., theworkflow, via the Type System 1110 shown in FIG. 11.

The CAS 210 principally provides for data modeling, data creation anddata retrieval functions. Data modeling preferably defines a treehierarchy of types, as was shown in FIG. 10A (and see as well FIG. 5).The types have attributes or properties referred to as features (FIG.6). In preferred embodiments, there are a small number of built-in(predefined) types, such as integer (ints), floating point (floats) andstrings. The data model is defined in the annotator descriptor, andshared with other annotators. A data modeling example is provided inFIG. 22. The exemplary data model 2200 provided includes an assembly oftypes including a Top 2210, Annotation 2220, Int 2230, POS 2240, Token2250, sentence 2260, preposition 2270, noun 2280, and other furthertypes 2290. The data model 2200 can be considered a combination of theinheritance structure, such as the exemplary single inheritancestructure disclosed in FIG. 10A, and the Component List, such as theexemplary Component List disclosed in FIG. 7.

CAS 210 data structures may be referred to as “feature structures.” Tocreate a feature structure, the type must be specified (see FIG. 5).Annotations (and other feature structures) are stored in indexes. Thefeature structures may be accessed via iterator(s) 1125 over the indexes(reference can again be made to FIG. 11).

FIGS. 33A and 33B illustrate exemplary pseudo-code that is useful forexplaining the operation of the CAS 210. This pseudo-code shows the useof the Type system and feature structures in the creation of a verb-typefeature structure, and its insertion into the CAS 210 index.

The CAS 210 may be considered to be a collection of methods (implementedas a class, for example, in Java or C++) that implements an expressiveobject-based data structure as an abstract data type. Preferably, theCAS 210 design is largely based on a TAE 130 Feature-Property Structure,that provides user-defined objects, properties and values forflexibility, a static type hierarchy for efficiency, and methods toaccess the stored data through the use of one or more iterators 1125(see FIG. 11).

The abstract data model implemented through the CAS 210 provides theUIMA 100 with, among other features: platform independence (i.e., thetype system is defined declaratively, independently of a programminglanguage); performance advantages (e.g., when coupling annotators 210written in different programming languages through a common data model);flow composition by input/output specifications for annotators 210 (thatincludes declarative specifications that allow type checking and errordetection, as well as support for annotators (TAE) as services models);and support for third generation searching procedures through semanticindexing, search and retrieval (i.e. semantic types are declarative, notkey-word based).

The CAS 210 provides the annotator 220 with a facility for efficientlybuilding and searching an analysis structure. The analysis structure isa data structure that is mainly composed of meta-data descriptive ofsub-sequences of the text of the original document 190A. An exemplarytype of meta-data in an analysis structure is the annotation. Anannotation is an object, with its own properties, that is used toannotate a sequence of text. There are an arbitrary number of types ofannotations. For example, annotations may label sequences of text interms of their role in the document's structure (e.g., word, sentence,paragraph etc), or to describe them in terms of their grammatical role(e.g., noun, noun phrase, verb, adjective etc.). There is essentially nolimit on the number of, or application of, annotations. Other examplesinclude annotating segments of text to identify them as proper names,locations, military targets, times, events, equipment, conditions,temporal conditions, relations, biological relations, family relationsor other items of significance or interest.

Typically an Annotator's 220 function is to analyze text, as well as anexisting analysis structure, to discover new instances of the set ofannotations that it is designed to recognize, and then to add theseannotations to the analysis structure for input to further processing byother annotators 220. For example, the specific inhibits relationshipdiscussed above in relation to FIG. 17 can be discovered by an annotator220 that is specifically designed identify this type of relationship, inthis case by recognizing that the phrase “may reduce the effectivenessof” implies an inhibitory relationship between the two chemical compoundnames before and after the phrase. Other phrases of a similar naturethat this particular annotator 220 may recognize as being inhibitory canbe “reduces the effects of” (see FIG. 24) and “suppresses the operationof”.

In addition to the annotations, the CAS 210 may store the originaldocument text, as well as related documents that may be produced by theannotators 220 (e.g., translations and/or summaries of the originaldocument). Preferably, the CAS 210 includes extensions that facilitatethe export of different aspects of the analysis structure (for example,a set of annotations) in an established format, such as XML.

In simple terms, a TAE Description is an object that describes a TAE130. In preferred embodiments, a TAE Descriptor is an XML document thatrepresents a TAE Description. The TAE Description contains all of theinformation needed to initiate and use the TAE. However, the TAEDescription does not specify, per se, how the TAE 130 will be deployed(for example, whether it will be tightly or loosely coupled).

The TAE Descriptions may exist in different states of completeness. Forexample, the developer of the TAE 130 may provide a TAE Description thatdefines the configuration parameters but does not set any of them. Theapplication developer then takes that TAE Description andprogrammatically assigns values for the parameters.

Common Analysis System 210 (CAS) Detail. The CAS 210 is that portion ofthe TAE 130 that defines and stores annotations of text. The CAS API isused both by the application and the annotators 220 to create and accessannotations. The CAS API includes, preferably, at least three distinctinterfaces. A Type system controls creation of new types and providesinformation about the relationship between types (inheritance) and typesand features. One non-limiting example of type definitions is providedin FIG. 5. A Structure Access Interface handles the creation of newstructures and the accessing and setting of values. A Structure QueryInterface deals with the retrieval of existing structures. More detailon the sub-components of the CAS 210 is now provided.

The Type system provides a classification of entities known to thesystem, similar to a class hierarchy in object-oriented programming.Types correspond to classes, and features correspond to membervariables. Preferably, the Type system interface provides the followingfunctionality: add a new type by providing a name for the new type andspecifying the place in the hierarchy where it should be attached; add anew feature by providing a name for the new feature and giving the typethat the feature should be attached to, as well as the value type; andquery existing types and features, and the relations among them, such as“which type(s) inherit from this type”.

Preferably, the Type system provides a small number of built-in types.As was mentioned above, the basic types are int, float and string. In aJava implementation, these correspond to the Java int, float and stringtypes, respectively. Arrays of annotations and basic data types are alsosupported. The built-in types have special API support in the StructureAccess Interface.

The Structure Access Interface permits the creation of new structures,as well as accessing and setting the values of existing structures.Preferably, this provides for creating a new structure of a given type;getting and setting the value of a feature on a given structure; andaccessing methods for built-in types. Reference may be had to FIG. 6,wherein exemplary feature definitions are provided for domains, eachfeature having a range.

In some embodiments, the creation and maintenance of sorted indexes overfeature structures may require a commit operation for featurestructures. On a commit, the system propagates changes to featurestructures to the appropriate indexes.

The Structure Query Interface permits the listing of structures(iteration) that meet certain conditions. This interface can be used bythe annotators 220 as well as by applications 170 in order to access theresults produced by the TAE 130. Preferably, this interface is intuitiveand facilitates reuse of the TAEs 130 in different applications 170.

There exist different techniques for constructing an iteration over thestructures in the CAS 210. First, in filtered iteration constraints orfilters on feature structures are constructed. Preferably, theseconstrain int and float values with inequality constraints; constrainstring values with equality; constrain the type of a structure; embedbasic constraints under paths; and, combine constraints with Booleanoperators AND, OR and NOT.

A new iterator 1125 may be employed where all elements in the iterationmeet the constraint. A special case of an iterator 1125 may exist forannotations, where it is preferable to iterate over annotations of sometype (e.g., sentence), and for each element in the iteration, list allannotations of another type (e.g., token) that are contained in the spanof the embedding annotation. Embedded structure iterators may beconstructed through filtered iterators. Providing a specialized API forthis purpose is both convenient and allows for an optimizedimplementation.

FIG. 33C is an example of pseudo-code for CAS 210-based data access, andshows the use of iteration over tokens.

In general, the underlying design of the TAE 130 recognizes threeprimary principles that encourage and enable component reuse; supportdistinct development roles insulating the algorithm developer fromsystem and deployment details; and, support a flexible variety ofdeployment options by insulating lower-level system middleware APIs.Aspects of implementation of these three principles are now discussed.

Encourage and Enable Component Reuse

Encouraging and enabling component reuse achieves desired efficienciesand provides for cross-group collaborations. Three characteristics ofthe framework for the TAE 130 address this objective. Thesecharacteristics are: recursive structure; data-driven; and,self-descriptive. Each one is described.

Recursive Structure: A primitive analysis engine 200, as illustrated inFIG. 2, is composed of an Annotator 220 and a CAS 210. The annotator 220is the object that implements the analysis logic (e.g., tokenization,grammatical parsing, entity detection). The annotator 220 reads theoriginal document content and meta-data from the CAS 210. The annotator220 then computes and writes new meta-data to the CAS 210. Similar to anested programming model, the aggregate analysis engine 300 is anexample of a recursive structure ensures that components may be reusedin combination with one another, while insulating their internalstructure.

Data-Driven: Preferably, an analysis engine's 200 processing model isstrictly data-driven. In the preferred embodiment, this implies that anannotator's 220 analysis logic may be predicated only on the content ofthe input, and not on the specific analysis engine(s) 200 that it may becombined with, or the control sequence in which the annotator 220 may beembedded. This ensures that an analysis engine 200 may be successfullyreused in different aggregate structures and different controlenvironments, as long as the annotator's input requirements are met.

The Analysis Sequencer 310 of FIG. 3 is a component in the frameworkresponsible for dynamically determining the next analysis engine 221,222, 223 to receive access to the CAS 210. The Analysis Sequencer 310 isdistinct from the Analysis Structure Broker 320, whose responsibility isto deliver the CAS 210 to the appropriate one of the text analysisengines 221, 222, 223, whichever it is, and wherever it may be located.The control logic of the Analysis Sequencer 310 is preferably separatefrom the analysis logic embedded in an Annotator 220, and separate fromthe Analysis Structure Broker's 320 concerns related to ensuring and/oroptimizing the CAS 210 transport. This separation of functionalityallows for the plug-and-play of different Analysis Sequencers 310. TheAnalysis Sequencer 310 enables simple iteration over a declarativelyspecified static flow to complex planning algorithms. Embodiments of theAnalysis Sequencer 310 can be limited to linear flows between theanalysis engines 221, 222, 223; however in more advanced applicationsdynamic and adaptive sequencing can be implemented. Accordingly, howmuch of the control specification is placed into a declarativerepresentation, and how much is implemented in the Analysis Sequencer310 for these advanced requirements is, among other things, applicationdependent.

Self-Descriptive: Ensuring that analysis engines 221, 222, 223 may beeasily composed to form aggregates and reused in different controlsequences is preferred for ensuring technical reusability. However, thismay not be sufficient for enabling and validating reuse within a broadcommunity of developers. To promote reuse, analysis engine 200developers are enabled to discover which analysis engines 221, 222, 223are available in terms of their capabilities.

Preferably, the data model of each analysis engine 200 is declared inXML, and then dynamically realized in the CAS 210 at run-time. In theUIMA 100, analysis engines 221, 222, 223 publish their inputrequirements and output specifications relative to this declared datamodel, and this information is used to register the analysis engines221, 222, 223 in an analysis engine directory service. This servicepreferably includes a human-oriented interface that allows applicationdevelopers to browse and/or search for analysis engines that meet theirneeds.

Support Distinct Development Roles

Various development roles have been identified, and taken into accountin the UIMA 100. Included are independent sets of interfaces in supportof different developer skill sets.

For example, language technology researchers that specialize in, forexample, multi-lingual machine translation, may not be highly trainedsoftware engineers, nor be skilled in the system technologies requiredfor flexible and scaleable deployments. One aspect of the UIMA 100provides for efficient deployment of their work in a robust andscaleable system architecture.

As another example, researchers with ideas about how to combine andorchestrate different components may not themselves be algorithmdevelopers or systems engineers, yet need to rapidly create and validateideas through combining existing components. Further, deploying analysisengines 221, 222, 223 as distributed, highly available services or ascollocated objects in an aggregate system requires yet another skill.

Accordingly, certain development roles have been identified. The UIMA100 therefore may make use of independent sets of interfaces in supportof different skill sets, such as the foregoing. These are now reviewed.

Annotator Developer: The annotator developer role is focused ondeveloping core algorithms ranging from statistical language recognizersto rule-based named-entity detectors to document classifiers.

The framework design ensures that the annotator developer need notdevelop code to address aggregate system behavior or systems issues likeinteroperability, recovery, remote communications, distributeddeployment, etc,. Instead, the framework provides for the goal offocusing on the algorithmic logic and the logical representation ofresults.

This goal is achieved through using the framework of the analysis engine200 and by requiring the annotator developer to understand only threeinterfaces, namely the Annotator interface, the Annotator Contextinterface, and the CAS interface. Preferably, the annotator developerperforms the following steps: implement the Annotator interface; encodethe analysis algorithm using the CAS interface to read input and writeresults and the AnnotatorContext interface to access resources; writethe Analysis Engine Descriptor; and, call the Analysis Engine Factory.

To embed an analysis algorithm in the framework, the annotator developerimplements the Annotator interface. Preferably, this interface is simpleand requires the implementation of only two methods: one forinitialization and one for analyzing a document.

It is only through the CAS 210 that the annotator developer accessesinput data and registers analysis results. As was noted previously, theCAS 210 may contain the original document (the subject of analysis),plus the meta-data contributed by any analysis engines 221, 222, 223that have run previously. This meta-data may include annotations overelements of the original document. The CAS 210 input to an analysisengine 220 may reside in memory, be managed remotely, or shared by othercomponents.

Preferably, all external resources, such as dictionaries, that anannotator needs to consult are accessed through the Annotator Contextinterface. The exact physical manifestation of the data can therefore bedetermined by the deployer, as can decisions about whether and how tocache the resource data.

In a preferred embodiment the annotator developer completes an XMLdescriptor that identifies the input requirements, outputspecifications, and external resource dependencies. Given the annotatorobject and the descriptor, the framework's Analysis Engine Factoryreturns a complete analysis engine 220.

Analysis Engine Assembler. The analysis engine assembler createsaggregate analysis engines through the declarative coordination ofcomponent analysis engines. The design objective is to allow theassembler to build an aggregate engine without writing code.

The analysis engine assembler considers available engines in terms oftheir capabilities and declaratively describes flow constraints. Theseconstraints are captured in the aggregate engine's XML descriptor, alongwith the identities of selected component engines. The assembler inputsthis descriptor in the framework's analysis engine factory object and anaggregate analysis engine is created and returned.

Analysis Engine Deployer. The analysis engine deployer decides howanalysis engines and the resources they require are deployed onparticular hardware and system middleware. The UIMA 100 preferably doesnot provide its own specification for how components are deployed, nordoes it mandate the use of a particular type of middleware or middlewareproduct. Instead, the UIMA 100 provides deployers the flexibility tochoose the middleware that meets their needs.

Insulate Lower-Level System Middleware

Human Language Technologies (HLT) applications can share variousrequirements with other types of applications. For example, they mayneed scalability, security, and transactions. Existing middleware suchas application servers can meet many of these needs. On the other hand,HLT applications may need to have a small footprint so they can bedeployed on a desktop computer or PDA, or they may need to be embeddablewithin other applications that use their own middleware.

One design goal of the UIMA 100 is to support deployment of analysisengines 221, 222, 223 on any type of middleware, and to insulate theannotator developer and analysis engine assembler from these concerns.This is done through the use of Service Wrappers and the AnalysisStructure Broker 320. The analysis engine interface specifies that inputand output are done via a CAS 210, but it does not specify how that CAS210 is transported between component analysis engines. A service wrapperimplements the CAS serialization and de-serialization necessary for aparticular deployment. Within an Aggregate Analysis Engine 300,components may be deployed using different service wrappers. TheAnalysis Structure Broker 320 is the component that transports the CAS210 between these components, regardless of how they are deployed.

The CAS 210 can be considered to be either loosely coupled or tightlycoupled. A loosely coupled CAS 210 is one that represents one typesystem that is distributed over more than one memory, and may beencountered in, for example, a networked application of the UIMA 100. Inthis case the annotators, such as annotators 410-470, work in more thanone memory. A tightly coupled CAS 210 is one that represents one definedtype system located in one memory (or one machine), where theannotators, such as the annotators 410-470, share the same memory.

To support a new type of middleware, a new service wrapper and anextension to the Analysis Structure Broker 320 is preferably developedand plugged into the framework. The Analysis Engine 200 itself does notneed to be modified in any way.

For example, Service Wrappers and Analysis Structure Broker 320 on topof both a web services and a message queuing infrastructure have beenimplemented. Each implementation involves different aspects and featuresregarding the specifics of the deployment scenarios. In general, webservices include those applications that communicate by exchanging XMLmessages.

Generally, the UIMA 100 treats the User Interface (UI) as anapplication-specific component. How applications accept input,communicate results or dialog with the user are determined by theapplication 170.

IV. System Interfaces

Various interfaces between top-level components of the UIMA 100 are nowdescribed. FIG. 23 provides a diagram similar to FIG. 1, however, FIG.23 further includes aspects of the UIMA 100 interfaces, which are showncollectively as the text intelligence system 108. A more detailed lookat aspects of the interface 115 between the application 170 and thesearch engine 110 is provided in FIG. 24. Other interfaces and the dataflow carried by the interfaces are also shown. For example there is aninterface 125 between the application 170 and the document store 120, aninterface 135 between the application 170 and the TAE 130, an interface185 between the application 170 and the knowledge access (structuredinformation) 180, and an interface 175 between the application 170 and adirector service 105 that includes a knowledge directory service 106 anda text analysis directory service 107.

Certain conditions are presented to assist with the description of theinterface 115. For example, Views support multiple tokenizations whereasSpans are used to annotate ranges within a view. An example of aSpan-based queries includes a query to find documents where a “title”field contains an “inhibits” relation. An exemplary result would be adocument 190A containing “Ibuprofen reduces the effects of aspirin onvascular dilation.” In preferred embodiments, various query languagesmay be used to define a span-based query. Preferably, an application 170may use the search engine 110 during pre-processing and run-time (orquery time).

During pre-processing the application 170 may retrieve documents, viathe Text Intelligence System 108, from the document source 120 throughinterface 125 and pass them to one or more of the TAEs 130 over theinterface 135. The TAE 130 returns the results in an analysis structurein the form of annotations on spans of tokens in the original textand/or other aggregate structures (for example, candidate glossaryitems, summarizations, or categorizations). With these results theapplication 170 may choose to add all or some of the discovered entitiesinto the index for the search engine 110 so that these entities may bereadily accessible during query time.

The search engine 110 provides to the application 170, via interface115, means for identifying a View, and the application 170, viainterface 115, pass entities, in a specified format, to the searchengine 110 for indexing. To support a powerful integration of textanalysis and search, the UIMA 100 expects that the search engine 110provide the ability to index annotations over spans. For example,consider a semantic entity, “$US President”, the search engine's 110indexing interface allows the application 170 to index the semanticentity “$US President” over a span of tokens such as “John QuincyAdams”.

At query time, the application 170 uses the query interface 115 of thesearch engine 110 for specifying Boolean queries. To support a powerfulintegration of text analysis and search, the UIMA 100 expects that thesearch engine 110 provide a query language over spans, and the interfaceenables the application 170 to perform queries. For example, a query mayseek all documents where the title (an annotated span) contains a USPresident (an annotated span), or seek all documents where the abstract(an annotated span) of the document contains “an inhibits” relation (anannotated span) that contains a qualifier (an annotated span) thatcontains the text “in vitro.”

Turning to the interface 135 between the TAE 130 and the Search Engine110, preferably, the TAE 130 is fed one or more documents by theapplication 170. Preferably the TAE 130 does not use the search engine110 to locate documents. The TAE 130 produces annotations that theapplication 170 may seek to index, but the TAE 130 does not determinewhat is indexed, nor does it communicate directly to the indexingfunction of the application 170.

Preferably, the relationship between the application 170 and TAE 130 issuch that neither one influences the state of the other. The application170 preferably includes a programming model and operators for managingstate across results for calling the TAE 130. Any shared/updateablestate is preferably managed by the UIM infrastructure, and not directlyby the TAE 130. For example, one suitable rule may be that “No sharedglobal variables exist between the TAE and the application.”

V. Two-Level Searching

Preferably, the UIMA 100 is aided by searching techniques that make useof a two-level evaluation process or model. This process is nowdescribed an exemplary manner, and is not to be construed as beinglimiting of the invention herein.

In some embodiments the evaluation model assumes a traditional invertedindex for in which every index term is associated with a posting list.This list contains an entry for each document in the collection thatcontains the index term. The entry contains the document's uniquepositive identifier, DID, as well as any other information required bythe applicable scoring model, such as number of occurrences of the termin the document, offsets of occurrences, etc. Preferably, posting listsare ordered in increasing order of the document identifiers.

From a programming point of view, in order to support complex queriesover such an inverted index, it is considered preferable to use anobject oriented approach. Using this approach, each index term isassociated with a basic iterator 1125 object (a “stream reader” object)capable of sequentially iterating over its posting list. The iterator1125 can additionally skip to a given entry in the posting list. Inparticular, it provides a method next(id) that returns the first postingelement for which DID≧id. If there is no such document, the termiterator 1125 returns a special posting element with an identifierLastID that is larger than all existing DIDs in the index.

Boolean and other operators (or predicates) are associated with compounditerators 1125, constructed from the basic iterators 1125. For example,the next method for the operator A (OR) B is defined by therelationship:(A OR B).next(id)=min(A.next(id),B.next(id)).The (WAND) Operator:

The two-level approach disclosed herein makes use of a Boolean predicatethat is referred to for convenience as WAND, standing for Weak (AND), orWeighted (AND). WAND takes as arguments a list of Boolean variables X₁,X₂, . . . , X_(k), a list of associated positive weights, w₁, w₂, . . ., w_(k), and a threshold θ. By definition, (WAND) (X₁, w₁, . . . X_(k),w_(k), θ) is true if:

$\begin{matrix}{{{\sum\limits_{1 \leq i \leq k}{x_{i}w_{i}}} \geq \theta},} & (1)\end{matrix}$where x_(i) is the indicator variable for X_(i), that is

$x_{i} = \left\{ \begin{matrix}{1,{{if}\mspace{14mu} X_{i}\mspace{14mu}{is}\mspace{14mu}{true}}} \\{0,{otherwise}}\end{matrix} \right.$

It can be observe that (WAND) can be used to implement (AND) and (OR)via:AND (X₁, X₂, . . . X_(k))≡WAND(X₁, 1, X₂, 1, . . . X_(k), 1, k),andOR (X₁, X₂, . . . X_(k))≡WAND(X₁, 1, X₂, 1, . . . X_(k), 1, 1).

Note that other conventions can be used for expressing the (WAND), e.g.,the threshold can appear as the first argument.

Thus, by varying the threshold (WAND) can move from being substantiallyan (OR) function to being substantially an (AND) function. It is notedthat (WAND) can be generalized by replacing condition (1) by requiringan arbitrary monotonically increasing function of the x_(i)'s to beabove the threshold, or, in particular, by requiring an arbitrarymonotone Boolean formula to be True.

FIG. 25 depicts the relationship of patterns with the WAND threshold,wherein a certain pattern is assigned a weight 2510, a second pattern isassigned a desired weight 2520, until the last pattern is assigned aweight 2530. Collectively the assignments 2510, 2520, 2530 are used toproduce a Threshold weight 2550. A summary of the use of the WANDtechnique 2800 is presented in FIG. 28. In FIG. 28, a first stepinvolves initializing 2810, then evaluating the weighted sum of patterns2820 and determining if the sum is above the threshold 2830. If the sumis below the threshold the pointers are advanced at step 2880 and theweighted sum of patterns evaluated again at step 2820. If the sum isabove the threshold the method conducts a detailed evaluation at step2840 and a determination at step 2850 if the value is above the minimumvalue in the heap (a heap of size n to keep track of the top n results,as discussed below). If not, control passes back to step 2880, otherwisethe result is inserted into the heap at step 2860, the threshold and/orweights are modified at step 2870, and control passes back to step 2880.

Generally, (WAND) iterates over documents. In some respects, WAND may beviewed as a procedure call, although it should also be considered asubclass of WF iterators with the appropriate methods and state. Assuch, (WAND) has a “cursor”′ that represents the current document, aswell as other attributes.

As is shown in FIG. 25, the arguments to WAND are patterns and weights.Patterns pat1, pat2, . . . are the typical patterns supported by WFimplemented as iterators 1125. Preferably, each pattern has anassociated positive weight, w, that may not be necessarily the sameduring the iteration. There is also a threshold weight w0.

In operation, WAND(w0, pat1, w1, pat2, w2, . . . ) returns the nextdocuments (wrt the current cursor) that matches enough of pat1, pat2, .. . so that the sum of weights over the matched patterns is greater thanw0.

More generally, each of pat1, pat2, . . . represents a Boolean functionof the content of the documents. Then, in operation, WAND(w0, pat1, w1,pat2, w2, . . . ) returns the next documents (wrt the current cursor)that satisfies enough of pat1, pat2, . . . so that the sum of weightsover the matched patterns is greater than w0.

Based on the foregoing discussion, it can be appreciated that wherepat_i represent an arbitrary Boolean function of the content of thedocument 190A, returned documents satisfy enough of pat1, pat2, . . . sothat the sum of weights over the satisfied functions pat1, pat2, . . .is greater than w0.

The sum of weights is not necessarily the score of the document.Preferably, the sum of weights is used simply as a pruning mechanism.The actual document score is computed by the ranking routine, takinginto account all normalization factors, and other similar attributes.Preferably, the use of a sum is arbitrary, and any increasing functioncan be used instead.

Consider the following example, while assuming that the pruning weightsand the score are the same:

Assume that a query is: <cat dog fight>

-   -   Cat pays $3    -   Dog pays $2    -   Fights pays $4    -   Cat near dog pays $10    -   Cat near fights pays $14    -   Dog near fights pays $12

The top 100 documents are desired. If at some point there exist 100documents with a score >=30, then a call is made where WAND(30, <cat>,3, <dog>, 2, <fights>, 4, LA(<cat>, <dog>), 10, LA(<cat>, <fights>), 14,LA(<dog>, <fights>), 12) where LA(X, Y) implements X NEAR Y.

In terms of implementation, the use of (WAND) is somewhat similar to theimplementation of AND. In some embodiments, the rules for “zipping”′ maybe as follows:

The entire WAND iterator 1125 has a cursor CUR_DOC that represents thecurrent match. It is desired to advance CUR_DOC.

Each pattern pat_i has an associated next_doc_i that represents where itmatches in a position>CUR_DOC.

Sort all the next_doc_i so thatnext_doc_i_1<=next_doc_i_2<=next_doc_i_3<= . . .

Let k be the smallest index such that w_i_1+w_i_2+ . . . +w_i_k>w_0.Then claim that it is possible to advance CUR_DOC to next_doc_i_k, andadvance all the other cursors to a position>=CUR_DOC. Now, if enoughweight at CUR_DOC is available, then CUR_DOC is returned. Otherwise thepositions are sorted again.

To understand this operation assume that the pattern pat_i matches everysingle document after next_doc_i. Even under this optimistic assumptionno document has enough weight before next_doc_i_k.

The following observations can be made.

-   -   1. A regular AND(X, Y, Z) is exactly the same as WAND(3, X, 1,        Y, 1, Z, 1). The two iterators 1125 will zip internally through        exactly the same list of locations, making exactly the same        jumps.    -   2. A regular OR(X, Y, Z) is exactly the same as WAND(1, X, 1, Y,        1, Z, 1). The two iterators will zip internally through exactly        the same list of locations, making exactly the same jumps.    -   3. If filter expression F is used that is an expression that        every document must match, then it can be implemented as        WAND(large_number+threshold, F, large_number, pat1, w1, . . . )

Various techniques may be used to set the pruning expressions, as theactual score is not simply a sum. These techniques preferably take intoaccount TF plus normalization.

Scoring

The final score of a document involves a textual score that is based onthe document textual similarity to the query, as well as other queryindependent factors such as connectivity for web pages, citation countfor scientific papers, inventory for e-commerce items, etc. To simplifythe exposition, it is assumed that there are no such query independentfactors. It is further assumed that there exists an additive scoringmodel. That is, the textual score of each document is determined bysumming the contribution of all query terms belonging to the document.Thus, the textual score of a document d for query q is:

$\begin{matrix}{{{Score}\left( {d,q} \right)} = {\sum\limits_{t \in {q\bigcap d}}{\alpha_{t}{w\left( {t,d} \right)}}}} & (2)\end{matrix}$

For example, for the tf×idf scoring model α_(t) is a function of thenumber of occurrences of t in the query, multiplied by the inversedocument frequency (idf) of t in the index and w(t,d) is a function ofthe term frequency (tf) of t in d, divided by the document length |d|.In addition, it is assumed that each term is associated with an upperbound on its maximal contribution to any document score, UB_(t) suchthat:UB_(t)≧α_(t) max(w(t, d1), (w(t, d2), . . . )

Thus, by summing the upper bounds of all query terms appearing in adocument, an upper bound on the document's query-dependent score can bedetermined as:

$\begin{matrix}{{{UB}\left( {d,q} \right)} = {{\sum\limits_{t \in {q\bigcap d}}{UB}_{t}} \geq {{{Score}\left( {d,q} \right)}.}}} & (3)\end{matrix}$

Note that query terms can be simple terms, i.e., terms for which astatic posting list is stored in the index, or complex terms such asphrases, for which the posting list is created dynamically during queryevaluation. The model does not distinguish between simple and complexterms; and each term provides an upper bound, and for implementationpurposes each term provides a posting iterator 1125. Given theseconditions the preliminary scoring involves evaluating, for eachdocument d:WAND(X₁, UB₁, X₂, UB₂, . . . , X_(k), UB_(k), θ)where X_(i) is an indicator variable for the presence of query term i indocument d, and the threshold θ is varied during the algorithm asexplained below. If (WAND) evaluates to True, then the document dundergoes a full evaluation. The threshold θ is preferably setdynamically by the algorithm based on the minimum score m among the topn results found thus far, where n is the number of requested documents.

The larger the threshold, the more documents are skipped and thus fullscores are computed for fewer documents. It can be readily seen that ifthe contribution upper bounds are accurate, then the final score of adocument is no greater than its preliminary upper bound. Therefore, alldocuments skipped by WAND with θ=m would not be placed in the topscoring document set by any other alternative scheme that uses the sameadditive scoring model.

However, as explained later, (a) in some instances, only approximateupper bounds for the contribution of each term might be available, (b)the score might involve query independent factors, and (c) a higherthreshold might be preferred in order to execute fewer full evaluations.Thus, in practice, it is preferred to set θ=F*m, where F is a thresholdfactor chosen to balance the positive and negative errors for thecollection. To implement this efficiently it is preferred to place a(WAND) iterator on top of the iterators associated with query terms.This is explained further below.

In general, the foregoing approach is not restricted to additivescoring, and any arbitrary monotone function in the definition of (WAND)can be used. That is, the only restriction is that, preferably, thepresence of a query term does not decrease the total score of adocument. This is true of all typical Information retrieval (IR)systems.

Implementing the WAND Iterator

The (WAND) predicate may be used to iteratively find candidate documentsfor full evaluation. The WAND iterator provides a procedure that canquickly find the documents that satisfy the predicate.

Preferably, the WAND iterator is initialized by calling the init( )function depicted in pseudo-code in FIG. 26. The method receives asinput the array of query terms, and sets the current document to beconsidered (curDoc) to zero. The method also initializes the currentposting posting[t] to be the first posting element in the posting list.After calling the init( ) function of FIG. 26, the algorithm repeatedlycalls WAND's next( ) method to get the next candidate for fullevaluation. The next( ) function takes as input a threshold θ andreturns the next document whose approximate score is larger than θ.Documents whose approximate score is lower than the threshold areskipped. FIG. 27 illustrates non-limiting pseudo-code for implementingthe next( ) function.

The WAND iterator maintains two invariants during its execution:

-   -   1. All documents with DID≦curDoc have already been considered as        candidates.    -   2. For any term t, any document containing t, with        DID<posting[t].DID, has already been considered as a candidate.

Note that the init( ) function establishes these invariants. The WANDiterator repeatedly advances the individual term iterators until itfinds a candidate document to return. This could be performed in a naivemanner by advancing all iterators together to their next document,approximating the scores of candidate documents in DID order, andcomparing to the threshold. This method would, however, be veryinefficient and would require several disk I/O's and relatedcomputation. The algorithm disclosed herein is optimized to minimize thenumber of next( ) operations and the number of approximate evaluations.This is accomplished by first sorting the query terms in increasingorder of the DID's of their current postings. Next, the method computesa pivot term, i.e., the first term in the order for which theaccumulated sum of upper bounds of all terms preceding it, including it,exceeds the given threshold (see line 5 and following in FIG. 27). Thepivot DID is the smallest DID that might be a candidate. If there is nosuch term (meaning the sum of all term upper bounds is less than thethreshold) the iterator stops and returns the constant NoMoreDocs.

To understand the significance of the pivot location, consider the firstinvocation of next( ) after init( ). Even if all terms are present inall documents following their current posting, no document preceding thepivot document has enough total contributions to bring it above thethreshold. The pivot variable is set to the DID corresponding to thecurrent posting of the pivot term. If the pivot is less or equal to theDID of the last document considered (curDoc), WAND picks a termpreceding the pivot term and advances the iterator past curDoc, thereason being that all documents preceding curDoc have already beenconsidered (by Invariant 1) and therefore the system should nextconsider a document with a larger DID. Note that this preservesInvariant 2. If the pivot is greater than curDoc, a determination ismade if the sum of contributions to the pivot document is greater thanthe threshold. There are two cases: if the current posting DID of allterms preceding the pivot term is equal to the pivot document, then thepivot document contains a set of query terms with an accumulated upperbound larger than the threshold and, hence, next( ) sets curDoc to thepivot, and returns this document as a candidate for full evaluation.Otherwise, the pivot document may or may not contain all the precedingterms, that is, it may or may not have enough contributions, and WANDselects one of these terms and advances its iterator to a locationgreater than or equal to the pivot location.

Note that the next( ) function maintains the invariant that all thedocuments with DID less than or equal to curDoc have already beenconsidered as candidates (Invariant 1). It is not possible for anotherdocument whose DID is smaller than that of the pivot to be a validcandidate since the pivot term by definition is the first term in theDID order for which the accumulated upper bound exceeds the threshold.Hence, all documents with a smaller DID than that of the pivot can onlycontain terms that precede the pivot term, and thus the upper bound ontheir score is strictly less than the threshold. It follows that next( )maintains the invariant, since curDoc is only advanced to the pivotdocument in the cases of success, i.e., finding a new valid candidatethat is the first in the order.

Preferably, the next( ) function invokes three associated functions,sort( ), findPivotTerm( ) and pickTerm( ). The sort( ) function sortsthe terms in non-decreasing order of their current DID. Note that thereis no need to fully sort the terms at any stage, since only one termadvances its iterator between consecutive calls to sort( ). Hence, byusing an appropriate data structure, the sorted order is maintained bymodifying the position of only one term. The second function,findPivotTerm( ), returns the first term in the sorted order for whichthe accumulated upper bounds of all terms preceding it, including it,exceed the given threshold. The third function, pickTerm( ), receives asinput a set of terms and selects the term whose iterator is to beadvanced. An optimal selection strategy selects the term that willproduce the largest expected skip. Advancing term iterators as much aspossible reduces the number of documents to consider and, hence, thenumber of postings to retrieve. It can be noted that this policy has noeffect on the set of documents that are fully evaluated. Any documentwhose score upper bound is larger than the threshold will be evaluatedunder any strategy. Thus, while a good pickTerm( ) policy may improveperformance, it does affect precision. In one embodiment, pickTerm( )selects the term with the maximal inverse document frequency, assumingthat the rarest term will produce the largest skip. Other pickTerm( )policies can be used as well.

Further reference in this regard may be had to commonly assigned U.S.Provisional Application No.: 60/474877, filed on even date herewith,entitled “Pivot Join: A runtime operator for text search”, by K. Beyer,R. Lyle, S. Rajagopalan and E. Shekita, incorporated by reference hereinin its entirety. For example, the monotonic Boolean formula may not beexplicit, as discussed above, but may be given by a monotonic black boxevaluation.

Setting the WAND Threshold

Assume that a user wishes to retrieve the top n scoring documents for agiven query. The algorithm maintains a heap of size n to keep track ofthe top n results. After calling the init( ) function of the WANDiterator, the algorithm calls the next( ) function to receive a newcandidate document. When a new candidate is returned by the WANDiterator, this document is fully evaluated using the system's scoringmodel, resulting in the generation of a precise score for this document.If the heap is not full the candidate document is inserted into theheap. If the heap is full and the new score is larger than the minimumscore in the heap, the new document is inserted into the heap, replacingthe document with the minimum score.

The threshold value that is passed to the WAND iterator is set based onthe minimum score of all documents currently in the heap. Recall thatthis threshold determines the lower bound that must be exceeded for adocument to be considered as a candidate, and to be passed to the fullevaluation step.

The initial threshold is set based on the query type. For example, foran OR query, or for a free-text query, the initial threshold is set tozero. The approximate score of any document that contains at least oneof the query terms would exceed this threshold and would thus bereturned as a candidate. Once the heap is full and a more realisticthreshold is set, only documents that have a sufficient number of termsto yield a high score are fully evaluated. For an AND query, the initialthreshold can be set to the sum of all term upper bounds. Only documentscontaining all query terms would have a high enough approximate score tobe considered as candidate documents.

The initial threshold can also be used to accommodate mandatory terms(those preceded by a ‘+’). The upper bound for such terms can be set tosome huge value, H, that is much larger than the sum of all the otherterms upper bounds. By setting the initial threshold to H, onlydocuments containing the mandatory term will be returned as candidates.If the query contains k mandatory terms, the initial threshold is set tok·H.

The threshold can additionally be used to expedite the evaluationprocess by being more opportunistic in terms of selecting candidatedocuments for full evaluation. In this case, the threshold is preferablyset to a value larger than the minimum score in the heap. By increasingthe threshold, the algorithm can dynamically prune documents during theapproximation step and thus fully evaluate fewer overall candidatedocuments, but with higher potential. The cost of dynamic pruning is therisk of missing some high scoring documents and, thus, the results arenot guaranteed to be accurate. However, in many cases this can be a veryeffective technique. For example, systems that govern the maximum timespent on a given query can increase the threshold when the time limit isabout to be exceeded, thus enforcing larger skips and fully evaluatingonly documents that are very likely to make the final result list.Experimental results indicate how dynamic pruning affects theefficiency, as well as the effectiveness of query evaluation using thistechnique.

Computing Term Upper Bounds

The WAND iterator requires that each query term t be associated with anupper bound, UB_(t), on its contribution to any document score. Recallthat the upper bound on the document score is computed by summing theupper bounds of all terms that the document contains. It is thereforeclear that if the term upper bounds are accurate, i.e., ∀t,UB_(t)≧α_(t)max_(d) w(t,d), then the upper bound on the score of adocument is also accurate i.e., it is greater than its final score. Inthis case, it guaranteed that, assuming the algorithm sets the thresholdat any stage to the minimum document score seen thus far, the two-levelprocess will return a correct ranking and accurate document scores.

It is straightforward to find a true upper bound for simple terms. Suchterms are directly associated with a posting list that is explicitlystored in the index. To find an upper bound, one first traverses theterm's posting list and for each entry computes the contribution of thisterm to the score of the document corresponding to this entry. The upperbound is then set to the maximum contribution over all posting elements.This upper bound is stored in the index as one of the term's properties.

However, in order to avoid false positive errors, it follows thatspecial attention should be paid to upper bound estimation, even forsimple terms. Furthermore, for complex query terms such as phrases orproximity pairs, term upper bounds are preferably estimated since theirposting lists are created dynamically during query evaluation.

In the following an alternative method for upper bound estimation ofsimple terms is described, as well as schemes for estimating upperbounds for complex terms. For simple terms, the upper bound for a term tis approximated to be UB_(t)=C·α_(t). Recall that α_(t) is determined bythe term idf and the term frequency in the query. C>1 is a constant thatis uniformly used for all terms. This estimate ignores other factorsthat usually affect the contribution of a specific term to thedocument's scores. These include term frequency in the document, thecontext of the occurrence (e.g., in the document title), document lengthand more.

The benefit of this estimate is its simplicity. The tradeoff is that thecomputed upper bound of a candidate document can now be lower than thedocument's true score, resulting in false negative errors. Such errorsmay result in incorrect final rankings since the top scoring documentsmay not pass the preliminary evaluation step and are thus not fullyevaluated. Note, however, that false negative errors can only occur oncethe heap is full, and if the threshold is set to a high value.

The parameter C can be fine tuned for a given collection of documents toprovide a balance between false positive errors and false negativeerrors. The larger C, the more false positive errors are expected andthus system efficiency is decreased. Decreasing C results in thegeneration of more false negative errors and thus decreases theeffectiveness of the system. Experimental data shows that C can be setto a relatively small value before the system effectiveness is impaired.

Estimating the Upper Bound for Complex Terms

As described above, the upper bound for a query term is estimated basedon its inverse document frequency (idf). The idf of simple terms caneasily be determined from the length of its posting list. The idf ofcomplex terms that are not explicitly stored as such in the index and ispreferably estimated, since their posting lists are created dynamicallyduring query evaluation. Described now is a procedure to estimate theidf of two types of complex terms. These procedures can be extended toother types of complex terms.

Phrases

A phrase is a sequence of query terms usually wrapped in quotes, e.g.“John Quincy Adams”. A document satisfies this query only if it containsall of the terms in the phrase in the same order as they appear in thephrase query. Note that in order to support dynamic phrase evaluationthe postings of individual terms also include the offsets of the termswithin the document. Moreover, phrase evaluation necessitates storingstop-words in the index.

For each phrase, an iterator is built outside WAND. Inside WAND, sincephrases are usually rare, phrases are treated as “must appear” terms,that is, only documents containing the query phrases are retrieved.Recall that the method handles mandatory terms by setting their upperbound to a huge value H, regardless of their idf. In addition, thethreshold is also initialized to H. Thus, only candidate documentscontaining the phrase will pass the detailed evaluation step.

Lexical Affinities

Lexical affinities (LAs) are terms found in close proximity to eachother, in a window of small size. The posting iterator of an LA termreceives as input the posting iterators of both LA terms, and returnsonly documents containing both terms in close proximity. In order toestimate the document frequency of an LA (t₁,t₂), the fact that theposting list of the LA is a sub-sequence of the posting lists of itsindividual terms is made use of. The number of appearances of the LA inthe partial posting lists of its terms traversed so far is counted andextrapolated to the entire posting lists.

More specifically, the document frequency of the LA is initialized todf₀(LA)=min(df(t₁),df(t₂)), and is updated repeatedly after traversingan additional k documents. Let p(t_(i)) be the posting list of termt_(i) and p′(t_(i)) be its partial posting list traversed so far. Let#(LA|p′(t_(i))) be the number of documents containing the LA in p′(t_(i)). The number of documents containing the LA in the entire postinglist of t_(i) can be estimated by the extrapolation:

${\#\left( {{LA}\text{❘}{P\left( t_{i} \right)}} \right)} = {\frac{\#\left( {{LA}\text{❘}{p^{\prime}\left( t_{i} \right)}} \right)}{{p^{\prime}\left( t_{i} \right)}}\left( {{p^{\prime}\left( t_{i} \right)}} \right)}$

It follows that the update rule for the document frequency of the LA atstage n is:

${{df}_{n}({LA})} = {\min\left\lbrack {{{df}_{n - 1}({LA})},\frac{{\#\left( {{LA}\text{❘}{p\left( t_{1} \right)}} \right)} + {\#\left( {{LA}\text{❘}{p\left( t_{2} \right)}} \right)}}{2}} \right\rbrack}$

The rate of convergence depends on the length of the term posting lists.It has been found that the document frequency estimation of LA quicklyconverges after only a few iterations.

Results

What follows is a description of results from experiments conducted toevaluate the presently preferred two-level query evaluation process. Forthese experiments, a Java search engine was used. A collection ofdocuments containing 10 GB of data consisting of 1.69 million HTML pageswas indexed. Both short and long queries were implemented. The querieswere constructed from topics within the collection. The topic title forshort query construction (average 2.46 words per query) was used, andthe title concatenated with the topic description for long queryconstruction (average 7.0 words per query). In addition, the size of theresult set (the heap size) was used as a variable. The larger the heap,the more evaluations are required to obtain the result set.

The independent parameter C was also varied, i.e., the constant thatmultiplies the sum of the query term upper bounds to obtain the documentscore upper bound. It can be recalled that the threshold parameterpassed to the WAND iterator is compared with the documents' score upperbound. Documents are fully evaluated only if their upper bound isgreater than the given threshold. C, therefore, governs the tradeoffbetween performance and precision; the smaller C, the fewer is thenumber of documents that are fully evaluated, at the cost of lowerprecision, and vice versa. For practical reasons, instead of varying C,C may be fixed to a specific value and the value of the threshold factorF that multiplies the true threshold can be varied and passed to theWAND iterator. The factor C is in inverse relation to F, thereforevarying F is equivalent to varying C with the opposite effect. That is,large values of F result in fewer full evaluations and in an expectedloss in precision. When setting F to zero the threshold passed to WANDis always zero and thus all documents that contain at least one of thequery terms are considered candidates and fully evaluated. When settingF to an infinite value, the algorithm will only fully evaluate documentsuntil the heap is full (while θ=0). The remainder of the documents thendo not pass the threshold, since θ·F will be greater than the sum of allquery term upper bounds.

The following parameters can be measured when varying values of thethreshold factor. (a) Average number of full evaluations per query. Thisis the dominant parameter that affects search performance. Clearly, themore full evaluations, the slower the system. (b) Search precision asmeasured by precision at 10 (P@10) and mean average precision (MAP). (c)The difference between the search result set obtained from a run with nofalse-negative errors (the basic run), and the result set obtained fromruns with negative errors (pruned runs). It can be noted that documentsreceive identical scores in both runs, since the full evaluator iscommon and it assigns the final score; hence the relative order ofcommon documents in the basic set B and the pruned set P is maintained.Therefore if each run returns k documents, the topmost j documentsreturned by the pruned run, for some j less than or equal to k, will bein the basic set and in the same relative order.

The difference between the two result sets was measured in two ways.First it was measured using the relative difference, given by theformula:

$\frac{{B/P}}{B} = {\frac{k - j}{k}.}$

Second, since not all documents are equally important, the differencewas measured between the two result sets using MRR (mean reciprocalrank) weighting. Any document that is in the basic set, B, in position iin the order, but is not a member of the pruned set, P, contributes 1/ito the MRR distance. The idea is that missing documents in the prunedset contribute to the distance in inverse relation to their position inthe order. The MRR distance is normalized by the MRR weight of theentire set. Thus:

${{MRR}\left( {B,P} \right)} = {\frac{\sum\limits_{{i = 1},{d_{i} \in {B - P}}}^{k}{1/i}}{\sum\limits_{i = 1}^{k}{1/i}}.}$Effectiveness and Efficiency

In a first experiment, the number of full evaluations was measured as afunction of the threshold parameter F. Setting F to zero returns alldocuments that contain at least one query term. The set of returnedcandidate documents are all then fully evaluated. This technique wasused to establish a base run, and provided that, on average, 335,500documents are evaluated per long query, while 135,000 documents areevaluated per short query. FIG. 29 shows the number of full evaluationsas a function of the threshold factor F, for long and for short queries,and for a heap size of 100 and 1000. FIG. 29 indicates that for allruns, as F increases, the number of evaluations quickly converges to thenumber of required documents (the heap size). Additionally, the averagequery time as a function of F was measured and was shown to be highlycorrelated with the number of full evaluations (correlation is higherthan 0.98 for all runs). For instance, for long queries, a heap size of100, and F=0, the average time per query of the base run is 8.41seconds. This time decreases to 0.4 seconds for large F values. Notethat the base run is an extreme case where no pruning is performed. Thethreshold can actually be set to a higher value before any negativeerrors occur. Based on these experiments, it can be seen that athreshold of approximately 0.8 results in significant pruning of thenumber of full evaluations with no effect on the result list.

FIG. 30 shows the difference between the pruned results and the baseresults for the same runs as measured by the MRR distance measure. Forsmall values of F the distance is zero since there are no false negativeerrors. Increasing F increases the number of false negative errors,hence the distance increases.

FIG. 31 shows the precision of the same runs, as measured by P@10 andMAP, for short and long queries with a heap size of 1000. It can be seenthat while MAP decreases as pruning is increased (as expected), P@10moderately decreases for short queries and only after very significantpruning. For long queries, the change in P@10 is negligible. Forinstance, when F=6.0, P@10 is not affected at all for both long andshort queries while the number of full evaluations is less than 1700(only 700 evaluations more than the 1000 required to initially fill theheap) and the MRR is approximately 0.5.

The reason for high precision in the top results set, even underaggressive pruning, is explained by the fact that a high threshold inessence makes WAND function like an AND, returning only documents thatcontain all query terms. These documents are then fully evaluated andmost likely receive a high score. Since the scores are not affected bythe two-level process, and since these documents are indeed relevant andreceive a high score in any case, P@10 is not affected. On the otherhand, MAP, that also takes into account recall, is detrimentallyaffected due to the many misses.

It may thus be assumed that by explicitly evaluating only documentscontaining all query terms, the system can achieve high precision in thetop result set. WAND can readily be instructed to return only suchdocuments by passing it a threshold value that is equal to the sum ofall query term upper bounds (referred to for convenience as an AllTermsprocedure). While this approach proves itself in terms of P@10, therecall and therefore the MAP decreases, since too few documents areconsidered for many queries. A modified strategy (referred to as aTwoPass procedure) permits the use of a second pass over the termpostings, in case the first “aggressive” pass does not return asufficient number of results. Specifically, the threshold is first setto the sum of all term upper bounds; and if the number of accumulateddocuments is less than the required number of results, the threshold isreduced and set to the largest upper bound of all query terms that occurat least once in the corpus of documents, and the evaluation process isre-invoked.

Table 1 shows the results of WAND with some different threshold factors,compared to the AllTerms and the TwoPass runs. For F=0, WAND returns alldocuments that contain at least one of the query terms. For this run,since there are no false negative errors, the precision is maximal. ForF=1.0, the number of full evaluations is decreased by a factor of 20 forlong queries and by a factor of 10 for short queries, still without anyfalse negative errors and hence with no reduction in precision. ForF=2.0 the number of evaluations is further decreased by a factor of 4,at the cost of lower precision.

It can be seen that AllTerms improves P@10 significantly compared toWAND, both for short and for long queries, while MAP decreasessignificantly. For systems interested only in precision of the topresults, ignoring recall, the AllTerms strategy is a reasonable andeffective choice. The TwoPass run achieves remarkable results both forP@10 and MAP. A small cost is incurred in terms of execution time forthe second pass but it is negligible in most cases since the termpostings are most likely still cached in main memory from the firstpass. In any event, these results demonstrate the versatility andflexibility of the method in general and the WAND iterator inparticular. By varying the threshold the “strength” of the operator canbe controlled from an OR to an AND.

TABLE 1 P@10 and MAP of AllTerms and TwoPass runs compared to basicWAND. ShortQ LongQ WAND P@10 MAP #Eval P@10 MAP #Eval (F = 0) 0.368 0.24136,225 0.402 0.241 335,500 (F = 1.0) 0.368 0.24 10,120 0.402 0.24115,992 (F = 2.0) 0.362 0.23 2,383 0.404 0.234 3,599 AllTerms 0.478 0.187443.6 0.537 0.142 147 TwoPass 0.368 0.249 22,247 0.404 0.246 29,932

The foregoing discussion has demonstrated that using adocument-at-a-time approach and a two level query evaluation methodusing the WAND operator for the first stage pruning can yieldsubstantial gains in efficiency, with no loss in precision and recall.Furthermore, if some small loss of precision can be tolerated then thegains can be increased even further.

As was noted above, preferably there is provided at least one iteratorover occurrences of terms in documents, and preferably there is at leastone iterator for indicating which documents satisfy specific properties.The WAND employs at least one iterator for documents that satisfy theBoolean predicates X_1, X_2, . . . , respectively, and the WAND operatorcreates an iterator for indicating which documents satisfy the WANDpredicate.

The WAND operator maintains a current document variable that representsa first possible document that is not yet known to not satisfy the WANDpredicate, and a procedure may be employed to indicate which iterator ofa plurality of iterators is to advance if the WAND predicate is notsatisfied at a current document variable.

VI. Exemplary Embodiment & Considerations

FIG. 32 provides an illustration of an exemplary embodiment of the UIMA100, where it is shown in the context of a life sciences application 170for drug discovery. This non-limiting example depicts some of the manycomponents and interfaces with which the UIMA 100 can operate.

In the illustrated embodiment there exists a linguistic resources 3200component containing resources (e.g., MEDLINE, UMLS, biomedicaldata/testbeds) that are specific to the application 170. Various relatedloader utilities 3210 are also provided, as are a plurality ofapplication support components 3220.

The UIMA 100 is provisioned to include core text analysis annotators andpost-processing analyzer annotators 220, certain of which are specificto the exemplary life sciences application 170, such as MEDTAKMIsemantic analyzer and a bio-relation analyzer. The core text analysisfunction works with a JTalent text analyzer TAE 130. The text data store120 can be implemented with DB2™, and a DB2™ loader and access modules.The text search engine 110 can be based on JURU, a full-text searchlibrary written in Java.

As can be understood when considering FIG. 32, how components areorchestrated to solve problems (or build applications) is an importantaspect of the UIMA 100. In addition to defining a set of components, anUIMA 100 preferably includes a set of constraints that determine thepossible orchestrations of these components to build effectiveapplications.

The document store 120 can be considered as a component with aninterface that enables documents and document meta-data to be stored andmanaged on disk. For example, in one embodiment, a constraint dictatingthat the main application logic is responsible for determining whetheror not the TAE 130 should write document meta-data to the store 120 forthe purposes of recoverability or post-processing access to TAE results,is an architectural control constraint. Among other things, thisconstraint is intended to ensure that TAEs 130 do not arbitrarily decideto write data to the store without the application's knowledge, sincethe impact on the application's overall performance may be considerable.The UIMA 100 suggests that the application developers are best informedwith regard to the overall operating requirements of the application(e.g., tradeoff between performance and recoverability) and thereforeshould control it. This in turn may require that the TAE's interface beexpanded to allow the application 170 to communicate its requirementthat the TAE 130 write its intermediate results to the store 120.

In other embodiments, one may model software components and userrequirements to automatically generate annotation (annotator or TAE)sequences. This approach may insulate the user from having knowledge ofinterface-level details of the components, and focus only on theapplication's functionality requirements. Moreover, automatic sequencingcan assist the user in making decisions on how to cost-effectively buildnew applications from existing components and, furthermore, may aid inmaintaining already built applications.

Automatic sequencing has a role in the control and recovery ofannotation flow during execution. Specifically, the flow executer cancall upon the sequencer with details about the failure and ask foralternative sequences that can still consummate the flow in the newunforeseen situation. Re-sequencing allows the application to betransparent to runtime errors that are quirks of the distributeddeployment of UIM.

Some of the concerns underlying the selection of inter-componentcommunication methods are flexibility, performance, scalability andcompliance with standards. Accordingly, the UIMA 100, as part of istechnical interface descriptions, preferably identifies communicationmethods for component interaction. It is intended that UIMA 100 willexploit the application of existing distributing computing technologiesas required in various parts of the architecture.

Generally, the UIMA 100 supports a loosely coupled (i.e., distributed)architecture where components may exist in distinct address spaces onseparate machines and in different operating environments, andcommunicate via service-oriented methods. This approach is preferred forflexibility and scalability. However, tightly coupled architectures arealso well within the scope of this invention, and the UIMA 100 supportstightly coupled system architectural models as well.

For example, various components may require tightly coupledcommunications to ensure high levels of performance. One example is theTAE 130, wherein the annotators 220 typically work in a series as theyprocess a document stream.

The analysis structure is frequently accessed and updated throughout theoperation of the TAE 130. Fast access, update and transmission to thenext annotator could be critical especially for embedded text analysisapplications that require fast response time or when analysis is done atquery-time as a user waits for results. Under these conditions, tightlycoupled communications between annotators 220 over an in-memory analysisstructure may be used to achieve high, predictable performance levels.

Another consideration for loosely coupled systems is the developmentparadigm. Again, consider a TAE 130, that may contain many annotators220, each evolving in their own right, each with their own prerequisiteson the analysis structure. Ideally, the UIMA 100 supports thedevelopment of annotators 220 such that the developer can workindependently of the component communication method, and then place theannotator in different containers ideally suited for requisitedevelopment or deployment environment.

Whether UIMA 100 components communicate in a loosely-coupled ortightly-coupled variant, their control independence is a distinct andimportant issue. Ideally, UIMA interfaces should restrict componentlogic from predicating on external control patterns. The implication ofthis tenet is that a component be written to operate without failure inan asynchronous control environment. It should operate regardless of theparticular flow of the application 170 in which it may be embedded.

Expressed another way, the UIMA 100 is preferably data-driven.Components may fail to process an input because the input data does notsatisfy certain pre-conditions, but the component should not dependenton a particular process flow. The data-driven focus also generallyenables a highly distributed agent-based approach to UIMA 100implementation.

Based on the foregoing it can be appreciated that the UIMA 100 providesa modular text intelligence system that includes application interfacesincluding the at least one document store interface 125 coupled to theat least one document store 120. The document store interface 125receives at least one database specification and at least one datasource and provides at least one database query command. The UIMA 100further provides the at least one analysis engine interface 135 coupledto the at least one text analysis engine 130. The analysis engineinterface 135 receives at least one document set specification of atleast one document set and provides text analysis engine analysisresults. Through the application interface the application 170 specifieshow to populate the at least one document store 120, and specifies anapplication logic for selecting at least one document set and forspecifying processing of the selected document set by the at least onetext analysis engine 130. Also specified is the processing of theanalysis results, as well as at least one user interface. Theapplication specification occurs by setting at least one parameter thatincludes a specification of the common abstract data format for use bythe at least one text analysis engine. Also included is at least onesearch engine interface 115 for receiving at least one search engineidentifier of at last one search engine 110 and at least one searchengine specification. The search engine interface 115 further receivesat least one search engine query result.

One skilled in the art will recognize that the teachings herein are onlyillustrative, and should therefore not be considered limiting of theinvention. That is, and as mentioned above, the UIMA 100 may be usedwith a variety of information sources, many of which are not discussed.For example, a document can include both text and images, either staticor dynamic, and annotators can be provided for both text and image data.

Thus, it should be appreciated that the foregoing description hasprovided by way of exemplary and non-limiting examples a full andinformative description of the best method and apparatus presentlycontemplated by the inventor for carrying out the invention. However,various modifications and adaptations may become apparent to thoseskilled in the relevant arts in view of the foregoing description, whenread in conjunction with the accompanying drawings and the appendedclaims. However, all such modifications of the teachings of thisinvention will still fall within the scope of this invention. Further,while the method and apparatus described herein are provided with acertain degree of specificity, the present invention could beimplemented with either greater or lesser specificity, depending on theneeds of the user. Further, some of the features of the presentinvention could be used to advantage without the corresponding use ofother features. As such, the foregoing description should be consideredas merely illustrative of the principles of the present invention, andnot in limitation thereof, as this invention is defined by the claimswhich follow.

1. A method for processing stored data, comprising: storing a collectionof data units, said data units comprising documents; retrieving at leastone data unit in response to a query, the query comprising a searchoperator comprised of a plurality of search sub-expressions each havingan associated weight value; and outputting as a result to the query theretrieved at least one data unit, where said retrieved at least one dataunit has a weight value sum that exceeds a threshold weight value sum,where said search operator comprises a weighted AND function, wherevarying the threshold weight value sum varies the operation of theweighted AND function from being substantially a logical OR function tobeing substantially a logical AND function, where at least one of theweight value sum and the threshold weight value sum is variable during asearch.